Neuro

Zero-waste software development – an engine for growth

webmaster — Tue, 21 Nov 2023 06:11:20 +0000

The economics of software development have fundamentally changed. Cheap credit is less available, resource costs are increasing and skilled technology professionals are in short supply. At a time when demand for technology has never been greater, companies and their senior leadership are increasingly challenged in their technology delivery and digital transformations. This short paper focuses on how organisations can achieve cost and value transparency, reduce waste and close the gap between high-performing competitors by increasing agility and resilience while driving down the cost of building and delivering software – zero-waste software development.

People always link the idea of growth to the notion of ‘expansion’. But in the world of technology, growth is in fact better linked to ‘change’. By changing how software development is managed and shifting to a zero-waste software development approach, organisations release additional engines of growth faster and outperform competitors in the long run.

Summary

Dragonfly’s own research shows companies are accepting that there is up to a 20% increase in resource costs, if not more in some key areas. This is despite recent layoffs in technology sectors.
The disparate and global nature of software delivery means productivity is currently often inconsistent, with the root causes of poor performance unknown and undeterminable in most cases. Reasons include complexity, structure, silo culture, management and many more. The inability of organisations to use technology to routinely capture and analyse productivity and cost data means this remains the status quo, leaving them unable to measure business value from their technology teams
Forward-thinking companies are now starting to look for and adopt new operating models, e.g. value-streams, that can better control costs and improve commercial returns on technology investments. Value streams cut across and connect siloed business and technology capabilities providing end-to-end visibility of the activity flow, from customer request to delivery.
More than ever, this makes the application of new technologies and lean management practices relevant. These focus on initiatives and delivery methods that improve transparency, reduce waste and cost while increasing productivity and transparency across the technology supply chain.
This growing number of organisations have identified that they require better business intelligence on technology projects to achieve a move to zero-waste software development.

Dragonfly has developed a new set of offerings to address these productivity, transparency and cost-related challenges. Typically, we see a 10-20% productivity gain across our clients while drastically reducing the cost of reporting and IT governance.

By providing better business intelligence we offer immediate insight into how to reduce waste and achieve productivity gains. We automate manually intensive administrative tasks out-of-the-box, such as status and performance reporting, governance, and other key management processes. Importantly, we tell teams how they stack up across their company as well as their industry peers, and extend this to third parties to measure technology vendor performance and give a complete view of the technology value chain. This enables organisations to benchmark the value that technology projects deliver to the business and keep technology aligned with business and digital transformation goals.

The journey towards zero-waste software development

Dragonfly’s goal is to move the industry towards zero-waste software development driving down the cost of building and delivering software. Every engineering, quality and transformation team should spend their time working towards their goals without having the traditional overhead and waste of current practices. Through the practical application of technology (analytics, automation and AI), lean management, and agile methods, we are creating a industry-leading lean engineering and quality supply chain at the enterprise-level. In doing so, we will move our clients toward zero-waste software development.

Using our business intelligence platform neuro, Dragonfly can quickly and securely analyse an organisation’s supply chain, devops processes and software development practices. We can provide immediate insight into how to reduce waste and achieve productivity gains, while automating manually intensive administrative tasks, such as status and performance reporting, governance, and other key management processes – out-of-the-box. Importantly, we can tell teams how they stack up across their company and industry peers or how their technology vendors are performing, giving them a complete view of their technology value chain. This enables organisations to benchmark the value that technology projects deliver to the business and keep technology aligned with business and digital transformation goals.

Industry shift to zero-waste software development

Over the past 18 months, we have seen a changing set of behaviours from enterprise technology organisations. The focus is moving towards value, transparency and efficiency. Specifically:

Companies want to consume knowledge and expertise based on value and outcomes. They want better transparency from technology teams and vendors – it is essential that they know where the money is going and how much business value is being delivered.
Forward-thinking companies are now starting to look for and adopt new operating models that can better control costs and improve commercial returns on technology investments
Companies want embedded knowledge, skills and value – they want to avoid having knowledge simply walk out the door when a digital transformation ends
Companies are improving the speed and delivery of existing capabilities across the technology supply chain by utilising a technology and data-first approach. They are focusing on the maturity of their data, rather than processes, to better retain knowledge, manage at a lower overhead and create value faster, harmonising key business and technology capabilities.

Modernising the CXO and leadership experience

Our conversations with CXOs show the requirements of senior leadership are clearly evolving in line with increasing demand from digital transformations as they consume ever more resources and spend. Senior teams need better information and insight to track and show progress throughout the transformation journey, whilst avoiding information overload. Unfortunately, increased demand for data has not led to teams being able to deliver the reporting, governance and level of insight the business and stakeholders need or ask for.

The “asks” from most CXOs fall into five main requirements:

A clear set of goals and outcomes they can track
Visibility and transparency across their teams
Consistent and insightful reporting
In-depth governance that is not an afterthought
Information and insight that is easy to consume and gives the ‘so what’

Unfortunately, obtaining even the most basic information puts increased pressure on an already stretched and stressed layer of middle management. Valuable time is taken away from their day job of delivering value, with managers and team leaders losing 1 – 2 hours a day to reporting, answering questions, resolving issues and assigning work. This adds up to a big productivity loss.

Studies have shown that when people are stressed, accuracy suffers. The pressure to come up with the right answers quickly can lead to incorrect, biased and incomplete data flowing across teams and organisations. Gloria Phillips-Wren & Monica Adya’s article in Journal of Decision Systems 27 May 2020 highlights and draws the conclusion (from much supporting research) that the quality of management decisions, information processing, accuracy and judgement are directly impacted by information overload, time pressure, complexity and uncertainty. They also highlight the consequences of these pressures and place emphasis on the use of decision support systems (DSS).

The result is an increase in waste and cost, and a loss of productive time. Sadly, this is an all-too-common picture across the technology industry. This begs the question: if the management function across the complex supply chain is struggling, how can the rest of the eco-system of a modern enterprise work efficiently and effectively?

The Future Engineering and Quality Supply Chain/Value Chain?

Re-shaping the way some of the most sophisticated businesses deliver software and innovation is not an easy task, but it is possible. Technology is not enough; it still needs the human touch to deliver transformation in the technology delivery and management space, bridging the information and capability gap. To reshape outcomes, the approach and narrative needs to be refined. The focus is a story of people development, data maturity and use of technology. This is as opposed to reshaping functions or changing the management team. Through the establishment of a ‘data first’ culture, companies can fix their management and supply chain. Using neuro as the backbone, Dragonfly has created an ecosystem of services that support the reshaping and transformation of engineering, devops and quality management functions in organisations.

Our data science and consulting teams work closely with clients to continually train neuro to identify and automate new insights to bridge the information gap. This enables clients to consume knowledge easily, manage risk and gain a single view of the technology supply chain. In turn, neuro provides deep personalised insights that prevent information overload and give clear context by providing the appropriate lens, e.g. quality, while removing the operational cost and waste that arises from a dedicated reporting and governance team or within a busy delivery team. This ensures CXOs have access to deep technical and domain knowledge to answer any specific questions 24/7. Think of this as the ultimate command-and-control capability that harnesses disparate data sources to deliver value to CXOs, management and delivery teams on the frontline.

Guided Engineering and Quality Transformation Journey

Change can often be painful for all involved but if you invest in the appropriate tools and are willing to break old rules then you will be successful. Dragonfly is now working with clients in new ways, combining key elements into the transformation journey. While no specific element is unique, how we apply them is. For example:

People – We are looking at the type of behaviours, questions and decisions that make organisations successful based on data sets. This enables us to link cause and effect with an understanding of what good looks like. It becomes far easier to answer questions like “how do we go faster” and tie such questions to successful behaviours, management practices and the wider organisational culture.

Data – We focus on the end-to-end data picture, maturity and flow and major part this plays in improving behaviours and the decisions that make organisations successful – through better use of their data and knowledge, and making sure the right people have the right insights. This is founded in translating technical metrics into a meaningful business narrative that is easy and lightning-fast to consume moving beyond the current siloed approach.

Use of technology – We simplify and standardise how technology is used to manage teams across the tool chain, injecting expert technical knowledge at each stage of the lifecycle. We ensure a focus on continual enrichment and refinement of new sources of insight by wrapping neuro around the different tools used by different teams. With the ability to connect to any data source via an API or our enterprise data bus, neuro can normalise vast data sets and provide a uniform and standardised visualisation layer.

If you view your organisation as an F1 team, you learn how to go faster after every race by asking new and better questions. Questions need to be asked based on data, not human assumptions alone. The nature of questions will change as your understanding improves. In essence, you ask better questions as you discover new insights from your data, giving clear units of meaning. As the quality of questions improve, the speed and consistency of good decisions and outcomes increase. Dragonfly recently delivered an engineering review using neuro to baseline and define the subsequent transformation roadmap. We quickly provided detailed insights on what needed to change to the senior leadership and management team, why and the value and outcomes this would achieve. Please see the case study below.

The Way Forward

Each company’s journey is unique, but by combining technology, developing people, and establishing a data-first culture, a sustainable flow of value can be created to move organisations and the industry towards a goal of zero-waste software development – and all the benefits that this brings. This strategy connects clearly to the overall business goals of better cost control and improved commercial returns on technology investments. Companies need to equip themselves to deal with rising input costs and increasing scarcity of resources, while building greater operational resilience and allowing teams to be more flexible and adaptable to changing economic conditions. Put simply, they need to be able to reap the rewards of doing more with less.

We see a clear difference in outcomes achieved between businesses willing to innovate quickly and collaborate across value chains and those that are currently not of this mind set. Those that cut corners or ignore the changing needs of a digital-first business will only see greater productivity losses and fewer positive outcomes. As the competition advances, the price paid will increase as time goes on. A recent paper by Baillie Gifford offers interesting investor insight and conclusion on technology spend given the potential economic head wins in 2023 onwards. ‘It is becoming clearer that companies that embrace leading-edge technology will be fitter for whatever the future holds. This fitness is what is sold by companies tied to digital transformation. While the path of digital transformation is already well-trodden by growth investors its potency and duration are still underappreciated’

Our approach is only the start of what is possible, but one thing is now very clear: the economics of combining knowledge-based services with better business intelligence and technology is exceptionally cost effective. Why spend twice on technology and services, when the smart money and value goes on purchasing them as one? If you would like to know more please reach out to me at dan@wearedragonfly.co

Case Study

Leading Enterprise Technology Provider – Tech-Enabled Engineering Review

After delays to a number of key releases and ongoing production issues, this leading enterprise technology provider recognised the need to review and improve its engineering capability. Dragonfly was engaged to undertake an engineering review using Neuro and define and implement the subsequent transformation roadmap.

Client Profile

Globally dispersed teams
Non-standardised practices and rapidly scaling
Keen to gain insights into their engineering processes and adopt best practices to improve output
Increasing delivery risk with delays to key deliverables and new features
Known weaknesses in several aspects of the SDLC

Solution

Utilisation of Dragonfly’s engineering management platform Neuro to baseline current engineering practices and performance
Analysis of engineering processes via structured interviews and asset reviews (based on Dragonfly’s 100 point- engineering assessment model)
Identification of key gaps and improvement areas
Definition of a target engineering delivery model and associated roadmap with prioritised actions
Definition of KPIs and SLAs to enable ongoing engineering performance measurement (relating to velocity, time to value and quality)
Establishment of an appropriate governance and control framework

Outcomes

A solid understanding of engineering practices – good and bad – with a clear roadmap for improvement
Immediate insights and visibility of engineering performance via Neuro
Standardised framework for engineering delivery with closer collaboration between globally dispersed agile teams
Improved visibility, communication and stakeholder management across the organisation
Ongoing engineering performance monitoring incorporating tracked KPIs/SLAs
Provision of a centralised function for performance-monitoring and benchmarking, decision-making and investment in continuous improvement

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Combining Automation with a Human Touch: How to Harness Value Stream Management to Assess Technology Vendors

Dan Hooper — Mon, 19 Jun 2023 14:37:37 +0000

In today’s digital age, companies heavily rely on technology vendors to deliver cutting-edge solutions and support their operations. Ensuring the performance and value delivery of these vendors is crucial to maintaining a competitive edge and providing exceptional customer experiences. One easy and powerful way to do this is through the application of Value Stream Management (VSM). This article explores how companies can leverage VSM to measure vendor performance and drive continuous improvement to reduce cost and waste.

Understanding Value Stream Management (VSM)

Value Stream Management is a holistic methodology that focuses on visualising, analysing, and improving end-to-end flow of value in software development and delivery processes. It provides banks with insights into how work is being done, identifies bottlenecks, and enables optimisation for maximum efficiency and value creation.

Defining the Value Stream

To measure the performance of technology vendors effectively, companies need to define and map the value stream associated with the vendor’s services. The value stream represents the sequence of activities, tools, and resources involved in delivering value to the bank and its customers. By visualising and understanding this flow, companies can identify areas of improvement and measure vendor performance against specific value stream stages.

Establishing Key Performance Metrics

Once the value stream is defined, key performance metrics can be established which align with strategic objectives and customer expectations. These metrics may include lead time, cycle time, defect rates, delivery frequency, customer satisfaction, and adherence to regulatory requirements. The selected metrics provide meaningful insights into the efficiency, quality, and value delivery of the vendor’s services.

Implementing Measurement Mechanisms

To measure vendor performance accurately, banks should implement robust measurement mechanisms throughout the value stream. This may involve capturing data from various sources, such as application performance monitoring tools, incident management systems, customer feedback channels, and project management platforms. By integrating these data sources, a comprehensive view of vendor performance can be gained along with key areas for improvement.

Continuous Monitoring and Feedback Loops

VSM relies on continuous monitoring and feedback loops to drive improvement. Regular checkpoints and review sessions should be established to assess vendor performance against the defined metrics. These sessions provide an opportunity to identify bottlenecks, address issues promptly, and collaborate with vendors on improvement initiatives. Real-time monitoring tools and automated reporting can enhance visibility and facilitate data-driven decision-making.

Collaborative Improvement Efforts

Measuring vendor performance using VSM goes beyond performance evaluations; it also promotes collaborative improvement efforts. Companies and vendors should work together to identify process inefficiencies, streamline workflows, and optimise value delivery. Through open communication, sharing best practices, and fostering a culture of continuous improvement, all parties can benefit from positive change and enhanced performance.

Vendor Scorecards and Performance Reviews

VSM offers an easy way to implement vendor scorecards and conduct periodic performance reviews to measure vendor performance. These scorecards provide a quantifiable assessment of vendor performance against predefined metrics and enable a structured evaluation process. Performance reviews offer an opportunity to discuss progress, address challenges, and align vendor services with the bank’s evolving needs.

Combining Automation with a Human Touch

Defining value streams, establishing key performance metrics and implementing measurement mechanisms, can be time-consuming, complex and expensive for organisations. Using our VMS platform Neuro, Dragonfly has simplified this process by creating automated ‘lenses’ that provide targeted insights and intelligence at each stage of the delivery lifecycle. Our lenses are flexible and allow for an essential human touch to easily and accurately define value streams and their associated measurement frameworks.

No need for expensive consultants or in-depth vendor reviews – Neuro’s turnkey capability quickly connects to engineering tool chains and provides detailed insight into vendor performance – aligning this to organisational and industry benchmarks.

Furthermore, our data science and consulting teams work closely to continually train Neuro to identify and automate new insights to bridge any information gap. Neuro’s deep personalised insights prevent information overload and enable users to consume a single view of the vendor technology supply chain – through their chosen lens such as quality – 24/7. Think of this as the ultimate command-and-control capability that harnesses disparate data sources to deliver value to CXOs, management and delivery teams on the frontline.

Align your vendors with your strategic objectives in a rapidly evolving digital banking landscape at a fraction of the cost.

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How neuro makes commit risk predictions

Dan Johnson Maia — Thu, 15 Sep 2022 07:13:03 +0000

Every developer has heard of Git. Originally authored by Linus Torvalds in 2005 to track the development of the Linux kernel, Git has become the go to version control system for 99.99% of development teams on the planet. With such widespread adoption, it seems obvious that everyone from heads of development to testing and quality specialists would be making the most out of the mountains of git data generated during the software development cycle. This, however, isn’t the case. Trying to draw conclusions from random commits across dozens of branches manually is a long and arduous process that would, simply put, be a huge waste of time.

We saw the potential of leveraging this data but knew that we needed to collect and present it in a digestible way. Enter Neuro. Using machine learning we can look at a commit and establish its risk using metrics such as developer experience, lines added, the number of files affected, and many more.

Take control of your code

Take the role of a dev team leader. You know that your newest member, a junior developer fresh out of university, is more likely to introduce a bug than a senior developer that’s deeply familiar with the codebase. You also know which devs worked in which directories the most, their strengths and weaknesses, their areas of interest. In regards to regression testing, you have experience and knowledge that allows you to predict which tests are most likely to fail. You’ve maybe done a bit of risk-based prioritization to determine what to keep a closer eye on. Nearly all of this knowledge is instinct based. Neuro takes a much more data driven approach to identify where bugs are more likely to be introduced, and does this in real time for every new commit.

Where are all the bugs?

A few metrics that factor into risk predictions were mentioned above, however there are many more. Each of these fall into one of three levels of insight. The first level covers things like the number of lines modified, how many files and directories are affected by the commit etc. The second level concerns the people who worked on the commit. Research has shown that it’s not source code metrics, but people and organizational processes that are the best predictors for defects. As mentioned before, a team leader might be deeply familiar with each of their devs and instinctively know which commits are high risk, but by looking at git data such as recent experience, whether devs have experience working in the directory, and how many people made changes to a commit, Neuro can make predictions in real time for the risk of every commit. The third and final level of insight is looking at previous bugs. How many bugs were introduced last time this file was modified? Which commits are most likely to introduce regression bugs? Using data from these three levels of insight we’ve created a system that tracks every commit in a project and categorises it as Low, Medium or High risk.

Give your test team the power

So what does this mean for the average tester? Simply put, Neuro leverages the huge amount of git data generated with every commit to draw attention to risky commits. It takes the instinctive knowledge gained from years of experience with a dev team and backs it up with data that even the most inexperienced tester can use to inform testing.

Thank you for reading.

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Value Stream Management

Alejandro Benages — Tue, 26 Apr 2022 10:04:42 +0000

Issue management tools like Jira usually now provide an integrated way to manage requirements, tasks, and defects. Typically they also provide search functionality, agile boards, customisable workflows, and sometimes, time-tracking. However, they are increasingly being used as a tool to measure the value stream management of a software development organisation.

Value Stream Mapping

Value stream management is not a new technique, it has its origins in value stream mapping, like many process improvement techniques. It is a recognised part of the Six Sigma technique. In essence it is a lean-based approach to mapping the value added by different parts of the software development process, and to identify waste, and then manage it.

As issue management tools track the states of different types of work, they therefore provide a proxy for measuring where work is within a larger process.

Value stream map, metrics & dashboards

One example metric is the time that issues spend on average in each state. When this is compared between components, products and teams, it can provide useful insight to where inefficiencies are rooted. You can add various “time in state” charts to your various dashboards to see how long different tests, issues or other types of tracked ticket are spending in different statuses. You can also see how this changes over time.

Perhaps you would need to include other key performance indicators KPIs, let us know how we can help your business leaders, at neuro we are looking to the future by combining multiple data sets from across the toolchain with multiple machine learning models to provide far-reaching insights into the technology supply chain.

If you are wondering why you would want to do this – a key principle of lean is “just-in-time” working. The less time things spend between elaboration and business feedback in production – the more efficient you are!

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Zephyr Dashboards on an Agile Project.

Havvanur Aydin — Fri, 25 Feb 2022 13:50:09 +0000

WHY ZEPHYR IS USEFUL ON AN AGILE PROJECT DASHBOARD?

Before explaining the importance of a Zephyr dashboard on an agile project, we must know what the requirements are for the perfect agile environment. The most important concepts that define today’s software world are the speed of keeping up with change, complexity and uncertainty about the future. Therefore, this agility is a form of management used to turn these problems into efficiency. It is derived from constantly adapting to change, addressing complexity with simple and faster solutions, and trying to minimise uncertainty by determining a path forward based on previous experiences.

So, to catch up, agile environment teams should have some tools that support the requirements. Zephyr is one of them, it is a Jira plugin that can be easily integrated into Jira projects. It facilitates the control of the test stages by handling the test management processes of the projects in the context of quality. Zephyr enables you to control this agility by bringing together all the different aspects of test management and development processes. To explain the benefits of the Zephyr in combination with an agile project management dashboard, we can consider it through three main topics.

DOCUMENTATION: Solution of Complexity

An agile dashboard simplifies test management capabilities for any Jira project. When using with Zephyr, test cases can be created in Jira and executed as needed or as part of a test cycle. It is simple to access tests by customising each test separately with priorities the tests or adding components and labels to group them.

As for testing strategies, there can be cycles according to each release or each sprint. Under each cycle testers can put the test cases inside specific repositories (user stories). The test cycle allows us to see a very well-organised structure. This helps for a fast execution by the testers as it reduces manual effort for the team. During the cycle, testers can easily see the status of the executions – which tests are passed, which tests are failed in the release cycle and who is assigned to the tests. All of them are dynamically visible for everyone, which ensures real-time collaboration.

An important element of agile projects is that teams should always be open to change. If all activities are not managed and tracked, things will become chaotic, and it can also affect outputs. To avoid such situations, the documentation plays a huge role to centralise the testing.

VISIBLE HISTORY OF TESTS: Adaptable to Changes

In Zephyr, one of the most useful features is assigning the related tickets to any of the test cases. Why is this so important? As we know, tests are created for each possible case for a created feature. Whenever there is a bug after the execution, testers can assign them to the related tests. This allows for better understanding for any person in the team when they see the steps of an issue. Within each test case, it is clear what is blocking that test, what is the main story etc. Briefly testing activities will be less complicated and more visible when there are those related tickets.

For the projects that are managed with agility, any time the requirements can be changed, so the tool should always keep up with this. With Zephyr it is very simple. If the requirements change, testers can easily integrate the changes to the tests. Also, on each ticket the history part shows the changes with the date and the name of the person who made the changes – which is important to track the integrations.

On the other hand, each test can be used for any story, any sprint or any release. That allows a reusability of the test cases.

Briefly, each test case has the history of the execution list, this is an additional part to see overall each execution of the specific test, so a tester can know the test failed in which cycles/releases/sprints. Selecting the improvements and analysing the failures will take your project to another level of quality.

METRICS AND REPORTS: Minimising the Uncertainty

In an agile environment, the reports are very important for determining the ways to be followed up in the future. Zephyr A Zephyr dashboard automatically provides daily execution progress, test executions (by Test Cycles or Testers), number of executions per day, and many customisable details according to the selections.

As an agile project tool, it should be convenient for the team for managing results, monitoring and reporting. The reporting capabilities of Zephyr is well-structured. For instance, a report can be created to provide a complete end-to-end view or get the visibility you need with ready-to-use reports and customisable dashboards. They are updated in real-time, with all the desired information, from manual to automated testing.

For each sprint these reports will be useful to determine what should be fixed, improved or created for future sprints. This reduces the uncertainty of future work to help maintain agility.

Overall Zephyr is one of the most used test management tools that assures control and visibility for testers, automation engineers, scrum masters and product owners. Zephyr is not only providing continuous agility and efficiency for the team, but also helps to deliver at a higher quality and releases to be faster than expected.

Last but not least, our product neuro integrates with Zephyr, as well as other tools like Cucumber, Jenkins, X-Ray and base Jira – so you can see all your test management information in one place!A

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Dragonfly Quality Engineering Toolchain Report 2021

Adam Smith — Mon, 24 Jan 2022 15:57:04 +0000

Executive Summary

This survey of software practitioners identifies some key trends in the quality engineering toolchain:

Git is the most popular version control system and is likely to increase further in popularity.
The Atlassian ecosystem is the most popular toolset within the Issue Management, test management and service management categories.
Jenkins is the most popular continuous integration tool.
SonarQube is the most popular static code analysis tool.
The Microsoft stack (Team Foundation Server / Azure DevOps) has limited popularity, with the most popular use case being as a test management tool.

We found that about 50% of respondents pull this data into Microsoft Office products in order to put together reports and analytics. Finally we introduce the neuro tool that integrates with the popular systems in the toolchain and automated reporting and analytics. This reduces the cost and effort involved with quality monitoring and process improvement.

Introduction

Sometimes it seems like the tools we use for software quality and test engineering are continuously changing. People are always chasing the next best thing, and tools that will make them more productive and efficient. Two decades ago the tools we used were clunky Windows client applications, with very limited integration with other tools. This led to a surge in multi-use tools that locked us into specific vendor ecosystems for managing all of our requirements, tests and issues. Looking at the market in 2021 things are very different – we have web-based, responsive and usable tools. Not only that, but everything is API-enabled to allow us to spin up our own integrations.

At least, that’s what we thought – but we wanted to know what everyone else is doing – so we surveyed 75 people from different organisations about their toolchain. In this report we will present the results.

The respondents were from a variety of levels in their organisations. 2% of them were in executive or C-level positions, 11% of them were Heads of Testing or Quality, 15% Test or Quality Managers, 42% testing specialists of other types, ranging from test engineers to test coaches. The remaining 27% are in other technology roles. We will be referring to quality engineers throughout this report – and we mean anyone who is working on testing and quality.

Of the respondents, 27% decide which tools to use, 40% influence decisions about which tools to use, 20% approve tool decisions, and the remaining 10% don’t have direct influence on the tool selection.

We focused in this report on tools that relate to testing, but not test automation tools. We are more interested in the general tools, because test automation tools are usually chosen based on the underlying technology under test, whereas issue management systems are not. We also asked about source control, manual test management, service management, continuous integration, static code analysis, and reporting.

Version Control

It is difficult to imagine how we would develop software at the speed we do in 2021 without version control systems. The complexity of these is often abstracted into a tree and trunk diagram, but that is usually an over-simplification. In reality, each tip of the tree has multiple children that feedback into other tips. A better comparison than a tree is a directed acyclic graph. All version control systems provide some strategy for managing the complexity of multiple engineers working simultaneously on the graph, and needing to merge changes into their working copies.

Quality engineers generally did not use version control systems until test automation started to move away from commercial off-the-shelf tools with inbuilt IDEs. These days most quality engineers use general purpose programming languages with various open-source libraries to achieve test automation. They need to manage the versions of this code just like software developers, so version control skills are now critical for many in the profession. This also supports other modern automation practices, like including automated tests in a pipeline.

Without a doubt, Git is the winner in our survey. nearly 78% of respondents use Git, and just Git. A small number (7%) are still hanging on to subversion. We were surprised to see only a tiny proportion – less than 5% – using the Microsoft tools – Team Foundation Server and Azure DevOps.

Git has some great features. It is decentralised, so can be used without a central server, although that isn’t the main way people use it. It also provides cryptographic verification of history, which is immutable. These features are reminiscent of blockchain technology, although Git predates them by several years. We note that the largest group of respondents use a public cloud-hosted version control system.

In more likelihood, the prevalence of Git has been driven by the popularity of the Software as a Service (SaaS) systems wrapped around it by GitHub, GitLab, SourceForge and Atlassian’s BitBucket. On the other hand, only 31% of our respondents are using a public cloud system for their source control.

Stack Overflow conduct an annual developer survey and observed that the use of Git by respondents rose from 69% in 2015 to 93% in 2021, which supports our findings. We also noted that only 8% of respondents were planning to move away from their current tool, and only half of these were using Git, which indicates increasing adoption.

The first system designed to control multiple people working on the same code base originates from 1984 (Grune, 1984) at a university. This led to the release of CVS in 1985, and it was the dominant product until the release of Subversion (SVN) in 2001. SVN was built by CollabNet, a collaboration software provider. In 2005, the commercial CVS provider that donated licenses to the Linux project decided to stop. This led Linus Torvalds to evaluate alternative solutions, and he decided none were good enough for the world’s biggest open-source project. After two weeks of development he published the first release of Git.

Continuous Integration

The term Continuous Integration (CI) was first proposed in 1991, but it sometimes seems like it is still a practice many don’t fully embrace outside of development. CI tools are crucial for implementing modern quality engineering practices. Not only do they automate tasks but provide consistency and avoid human error. For quality engineers they provide a framework for executing unit tests as well as automated functional tests.

We found that the open-source Jenkins is the most popular tool, with 47% of respondents naming it as their current tool.

The next most popular tool was GitHub, at 15%. No other tool obtained more than 10% of respondents. Only 23% of respondents are using a public cloud system, with the majority using private cloud or on-premise systems. Again, 8% of respondents are planning to move away from their tool, but most of these seemed to be current Jenkins users, indicating its market share is likely to decline.

The 2020 JetBrains dev ecosystem survey found similar results, with 55% using Jenkins and 15% using GitHub Actions. They also found that 27% were using GitLab and had showed better results for Travis CI, Teamcity and GitLab – but we couldn’t reproduce those results.

The primary features required for a CI server are to support compiling code, running tests against the code and deployment of the code. Increasingly as infrastructure is being defined as code, it is not just software that is deployed through CI, but also servers and other infrastructure components.

Of the leading CI products available, there seems to be little difference in their ability to integrate with other tools, to manage different software ecosystems (cross-platform vs Windows), or even to be hosted on different operating systems. It seems the biggest difference is that Jenkins is open-source, and others are not. Some CI systems are SaaS only, which can give you less control over the build environment and make larger scale automated testing more challenging.

Researchers studied 246 GitHub projects that used some CI tool to investigate the productivity and quality impact of CI. They concluded that the use of CI:

Has a significantly positive effect on productivity when measured in terms of pull requests being accepted vs rejected.
Has no measurable effect on the amount of externally reported bug reports.

This is interesting because it shows that the use of CI alone does not drive quality. Google, however, does it right. They have 4.2 million tests running in their CI system. They calculated that they have 150 million test executions per day, which means each test runs on average 35 times a day.

So why do so many teams have a CI server, but not have effective tests running it? A systematic literature review by the University of Adelaide called out the following key challenges “Although automated testing is a key part of CI, many organisations studied were unable to automate all types of tests. Attributing this to lack of infrastructure and the time-consuming process of automating manual tests. Poor test quality, including unreliable tests, too many tests, low coverage, and long running tests.2

These issues obviously reduce confidence in the CI pipeline, which means it is relied upon less for testing, and simply becomes a build automation tool.

Issue Management

We weren’t particularly surprised to find that Jira is the most popular issue management tool out there. 69% of respondents mentioned Jira, Team Foundation Server and Azure DevOps were mentioned by 11%, and the remainder were not significant enough to mention statistically. One respondent used Excel, and two used Shortcut (previously clubhouse). One person still seems to be using MicroFocus ALM, but plans to move away. Not a single user of Jira identified a plan to move away from using it – indicating the pull of the Atlassian ecosystem.

Referring again to the 2020 Jetbrains dev ecosystem survey – they found similar results in terms of the Atlassian and Microsoft tools, but again showed better results for GitLab and GitHub. They also showed strong results for Trello, which is surprising given it is such a generic tool. Interestingly, in 2019, they also found that 61% of respondents used different tools for different teams within their organisation.

Around 31% of respondents are using a public cloud, and 21% are using on-premise versions, which is interesting given that Atlassian has retired support for on-premise Jira in favour of private cloud instances.

Issue management tools usually now provide an integrated way to manage requirements, tasks, and defects. Typically they also provide search functionality, agile boards, customisable workflows, and sometimes, time-tracking.

However, they are increasingly being used to measure the value streams of a software development organisation. Value stream management is not a new technique, it has its origins in manufacturing, like many process improvement techniques. It is a recognised part of the Six Sigma technique. In essence it is an lean-based approach to mapping the value added by different parts of the software development process, and to identify waste, and then manage it.

As issue management tools track the states of different types of work, they therefore provide a proxy for measuring where work is within a larger process. One example metric is the time that issues spend on average in each state. When this is compared between components, products and teams, it can provide useful insight to where inefficiencies are rooted.

Value stream management isn’t just about metrics though, it involves various process improvement techniques. Value stream mapping is the initial step, and includes seven tools:

Process activity mapping
Supply chain response matrix
Production variety funnel
Quality filter mapping
Demand amplification mapping
Decision point analysis
Physical structure volume and value

While these may sound like they are somewhat distant from software development, they generally apply well.

Test Management

Test management tools usually support the planning and tracking of manual test activities, sometimes including tracking of automated test activities.

Often they also contain some requirements management or issue management functionality. In this case, where they are multi-use tools the line is blurred with issue management tools. In fact, Jira plugins Zephyr Squad and X-Ray have a combined 31% of the test management market, and they integrate directly through the Jira web interface. This again indicates the strength of the Atlassian ecosystem.

Test management tools paint a very different picture to the rest of the toolchain. 18% of respondents don’t use any test management tool. There is also no clear market leader. The next most prevalent system after Atlassian was Excel, which is a limited tool for tracking testing, but can be enough. In this category, the Microsoft Stack (Azure DevOps and Team Foundation Server) did better than in other categories – with 10% of respondents using it for tests.

Around 21% of respondents are using a public cloud tool – consistent with other categories.

Service Management

IT Service Management tools are orientated towards handling incidents, service requests, problems and changes. Information from these tools is important to understand the quality of a system when it is in use.

Many respondents did not use a service management tool – in fact 40%. That makes service management the least popular tool to implement from our list. Jira Service Management and HP Service Now both received 20% of the mentions. With no other tools having a significant share. Jira’s Service Management offering is certainly suitable for startups but doesn’t have the scale required for a large corporate environment, where HP Service Now continues to be the most popular tool.

Most people that are planning to implement a different tool are either not using one at the moment, or are using Jira, which could indicate that some companies may have outgrown it.

Static Code Analysis

Static analysis is usually the use of a tool to analyse software without executing it. This is a useful tool for tracking metrics that may point to quality issues in software. These tools use data-driven or rule-based processes. Data driven approaches increasingly use machine learning to identify potential areas of concern.

Static code analysis is an important technique for increasing maintainability and improving security and nearly 80% of respondents identified a tool they use.

SonarQube is the most prevalent at 39%, with in-IDE tools the next most prevalent at 21%. Only 16% of respondents are using public cloud, which is indicative of the popularity of in-IDE rather than server-led systems.

Delivering feedback to developers in real time through in-IDE tools is good for the efficiency of the process, as the feedback is inline. This process does not provide system-wide quality metrics though, which a server-led solution typically supports.

Reporting and Analytics

In today’s data-driven world we are all trying to measure as much as possible. Especially with so much remote working. So how do people get usable information out of all these tools for insight and consumption?

Only 32% of respondents indicated they had integrated management information processes coming from these tools, which is low for any business process. 49% of respondents are using tools like Excel and PowerPoint to prepare reports, and 19% are using business intelligence platforms.

Interestingly, more people said they are writing custom code than are using the dashboards or reporting available from the data source tools themselves.

The average respondent had 2.59 of the integrations we asked them about. The super-integrators tend to use Jira, Jenkins and Veracode.

Integrating different tools together in the software toolchain can significantly improve the information that can be obtained from any one tool, and also improve usability.

We asked about the following integrations:

Integration of test automation into test management tools
Integration of issue management and test management tools
Integration of service management and issue management tools
Integration of static code analysis and CI tools
Integration of source control and issue management tools

People who use generic business intelligence software to craft their own reporting are more likely to have more integrations between their tools. The most popular integrations are issue management and source control, and issue management and test management. 62% of respondents have these integrations. The least popular integration is issue management and service management, with just 39% of respondents reporting.

Reporting and analytics functionality is crucial to understand the large volume of data coming from the modern quality engineering toolchain. This data can be used to monitor and control an individual test process, or to analyse retrospective data, or to compare it to industry benchmarks.

Nearly 20% are using general purpose business intelligence software for reporting. Configuring general and multi-purpose tools for reporting information specific quality engineering is often a time consuming activity. Using tools specifically designed for development and testing data can save a lot of that time.

In that context, it is notable that only 4% of respondents use the reporting functionality in the toolchain applications themselves. This shows that they are clearly not getting value from each tools individuals capabilities.

Speaking generally, analytics and reportings systems can:

Allow the dynamic analysis of value streams considering multiple sources of data.
Produce regular reporting on quality activities in presentable form.
Adapt to different contexts quickly — e.g. product, team, process.
Support quality improvement, or lean methods like value mapping.

Linking operational data about the code base to the issues, tests, incidents relating to that code enables predictive analytics. Increasingly, the data in these systems can be used with machine learning models to predict faults in your products and processes.

Neuro helps you harness your data from your toolchain to create high performing engineering and quality teams. It provides complete visibility of your engineering and quality performance.

Goal setting — Close the gap between insight and action. Set user-defined goals to track progress, identify roadblocks and align engineering and quality teams with busines priorities.
Consistency — Deliver consistency across your agile portfolio. Address the challenge of managing dependencies among teams, especially third parties.
Automated reporting layer — Provide rapid reporting across your toolstack. Smart dashboards provide greater insight and reduce administrative overhead with automated reporting.

Neuro integrates with Jira, Zephyr and X-Ray. It also consumes test automation meta-data from Cucumber and Jenkins, and source control history from Git-based products. Integrate your quality engineering tool-chain with built in integrations and a REST API for adding your own.

The post Dragonfly Quality Engineering Toolchain Report 2021 appeared first on Neuro.

Quality engineering managers, dashboards and remote teams.

Ezz El Defrawi — Fri, 07 Jan 2022 11:22:48 +0000

If the last two years have accelerated the shift towards remote work, then it is no surprise that after having moved so swiftly, our remote-ward leap has come with its own share of teething problems. Chief amongst them is how to get the best out of teams that have gone fully remote and have remained that way for extended periods of time. Gone are the in-person meetings, coffee break catch-ups and extended lunch time moans about management that used to keep the workplace machine ticking, and a quality engineering manager cannot refuse to change.

The shift to remote working

Over the last two years there have been hundreds of articles written around the phenomenon of remote working; with swings between celebrating the freedom it offers workers to outright horror at the loss of the creative x-factor found only, apparently, in the area surrounding the office water cooler. Amidst the partisan debate we have also experienced the rise of the hybrid model; with people coming into the office for 2/3 days and then again escaping to the freedom of their homes for the remainder of the week, a concept which the majority of firms are embracing in some form. However for those of us who work on international remote teams we are forced to struggle on as we are; taking zoom calls from whatever corner of the house we can find some small measure of quiet, desperately ignoring our ballooning electricity bills.

Leadership in the new normal

In spite of all of this, as leaders, supervisors or managers, we have a responsibility to our teams to give them the best leadership we can. To recognise when things are going well and react when disaster looms. Based on my experience from the 18 months I have gathered what I believe to be the most salient points in this article.

The first is that we need to be more comfortable with asynchronicity; of reducing the number of hours we spend in mass zoom calls and breaking things out so people can see them when they are free to. If the whole team does not live in the same time zone then a 9am stand-up is meaningless when half the people on it are dialling in during their lunch hour. Childcare can also come with its own set of inflexible time commitments, which can cause unnecessary stress if individuals feel their professional careers are being impacted.

Remote working is more than just Zoom or Teams

We should acknowledge that in many cases, and particularly for regular scheduled meetings, many of the invitees will either not participate or have no reason to. Why not have the discussion in the comments section of a ticket, or on a channel dedicated to the topic. Embrace documentation, charts, video walkthroughs and if you can think of a fourth option then embrace that too. People consume information in different ways and often have the most success when they are given agency to choose for themselves. As with all things though, moderation, age old aphorisms involving babies and bathwater apply here so be wary. While Zoom lectures might be one of the worst ways conceived to process information, there should still be space for verbal discussion, a chance to see and hear other people address an issue.

Feedback is essential

The opportunity to take on board team members’ opinions is also critical to remote working. Unlike in the office where comments can be quietly made or people’s reactions can be observed, the challenge of remote working is that frustrations can go unnoticed within the wider organisation. If people feel that they lack a forum to address their issues or, even worse, that their voices are not being heard, then the team dynamic will begin to unravel. This tends to manifest not as an immediate collapse, but more as a slow regression in work efficiency and a departure of veteran staff.

While by no means terminal, this kind of failure can be easily avoided by offering individuals the chance to lay their case before management. At times this can be included within a sprint’s rituals but must also be present outside of the Agile framework; in case there are macro issues that cannot be addressed by a single team, or within a single sprint.

How neuro can help

As important as it is to have communications flowing up the management chain it is equally important from a manager or team lead perspective to have a top-down understanding of what is going on within each area of responsibility. This should not be limited to a simple recitation of the epics / stories that are being worked on but should encompass the effectiveness of working practices; whether certain people are carrying too much or too little of the workload or if knowledge is puddling around certain members instead of being spread across the group.

While it is possible to trawl through issue tracking software such as Jira or Asana for this, a modern quality engineering manager would prefer to use a tool like neuro, which allows them to collect and blend the data points needed and construct an engineering manager dashboard that give an instant snapshot of team progress, with the possibility to break it down further to the individual level. From here it is up to the respective leader to identify, assess and then act to improve the functioning of their team, encouraging and praising those who have done well, lifting the burden on those who are doing too much and coaching those who need it.

Also Read: Uses of JIRA Project Management & Reporting Tool

Active quality engineering manager

Team management in-person is not a passive occupation, but with remote working it is even less so. A quality engineering manager must be proactive in hunting out issues within the team, whether they be workflow, team or delivery-related. From a personnel perspective there should be regular sense checks of how people are doing; spaced far enough apart that they themselves are not a source of frustration but in as diverse a set of mediums as possible; group discussions, 1-1s, written feedback and message boards (anonymous or otherwise). For those perfectionists reading this with a growing sense of dread the sad answer is that this process is never complete. There is no final victory to be had here, but instead a constant cycle of trial, adjustment and improved outcomes. People will cycle through the organisation, the time zones they work in will change and project goals will shift along with the technologies in use. You will have to adapt your approach to the one meshes best with the team you have currently, and the tasks assigned. This is often thankless and rarely simple but the payoffs for your remote team can be mighty indeed.

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CEO metrics to make an executive smile

Dan Hooper — Tue, 21 Dec 2021 14:14:17 +0000

Data driven testing, Jira burndown chart, Jenkins, Xray, GitHub, scrum project, or agile projects Now are all words that are foreign to most CEOs. Let alone the fact that they are very much a key part of the modern technology supply chain, which enables the business to compete as a modern enterprise. Modern CEOs clearly need to understand technology and how it is delivered and the value it creates. CTOs have traditionally been the fountain of all wisdom when it comes to technology.

The world is a different place. Gone are the days when technology was a budget black hole. The new world is all about transparency and value, and that means CEOs need to understand where the money is being spent and the value it is creating, to ensure companies are getting good value for their buck (a.k.a. revenue growth)

The business of executives dashboard.

We are all time-poor, but CEOs (being one myself) value their time highly and want to get straight to the point – and take the shortest journey to the answers they seek. This means with little knowledge CEOs can ask far more insightful questions of technology teams than in the past, but they need good data sources. This means you need to perform data driven testing, to get just the specific CEO KPIs to answer precise questions.

With data driven testing, you will have the right information to hand and to demonstrate you are ahead of the curve, but what are the right CEO metrics to place in front of your manager?

Performance Metrics, which ones really matter?

There are metrics and there are metrics that matter. There are testings, and data driven testings. There are many technical measurements that companies can use. Establishing a simple set of core performance metrics that provide the biggest bang for buck is the best place to start to keep your CEO informed. This enables you to educate them on what is important, without creating a lot of white noise, while having useful CEO dashboard metrics.

Below are our top five metrics that matters, and you can actually create with Neuro:

Burndown

A burndown chart representation of work left to do versus time. The outstanding work (or backlog) is often on the vertical axis, with time along the horizontal. Burndown charts are a run chart of outstanding work. It is useful for predicting when all of the work will be completed. This is a great metric for meeting specific goals. Machine learning can be used to provide more accurate forecasts based on various variables like work patterns. One of the greatest advantages of Neuro is its integration capabilities, discover how to create a Jira burndown chart or a burndown chart on GitHub.

Cycle Time

Cycle time is the amount of time from the creation of a work ticket to the time it is shipped. For example, the time is taken between a ticket being logged in Jira for a new button on an application, to the time it is deployed into production. This gives you the elapsed time it takes to complete one task. When looking at the overall project you can then see how quickly change is delivered. The shorter the cycle time the better.

Time In State

There is a whole raft of metrics that can be used as part of a Value Stream Management (VSM) approach to measuring the flow of value across the technology supply chain. The best one to start with is time in the state. This is how much time each ticket (task) spends in each phase or state is measured. This is a very powerful metric as it can be used to identify bottlenecks in a process, team or function.

Reopen rates

Reopen rate measures the amount of work needed in any task that is reopened once it has been closed or completed. For instance, a defect is set to test and then fails to test, it is re-opened and sent back to the developer. If for instance, you have a reopen rate greater than 10% in any one area, this would be of concern when looking at a project or group of projects. The number of reworked hours is also a very powerful metric at a project level which can be captured through combining time in the state with a reopen rate.

Insight

These are our picks for the top metrics from data driven tests to make your CEO smile. Perhaps you would need to include other key performance indicators KPIs, let us know how we can help your business leaders At neuro we are looking to the future by combining multiple data sets from across the toolchain with multiple machine learning models to provide far-reaching insights into the technology supply chain. Look out for exciting new features, for example how neuro integrates with Jira software.

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Governance dashboards provide the building blocks to automate your IT projects

Dan Hooper — Fri, 10 Dec 2021 13:18:14 +0000

Governance Dashboard

The long tail of the pandemic now appears set to continue into 2022, and companies are starting to focus their governance dashboard to be more effective and efficient next year to support business goals and growth. The journey continues for companies towards the new normal, be that 100% remote, or a hybrid working model. The legacy challenges that will remain and their impact are now becoming highly visible. A key challenge now becoming a major concern is governance around technology change.

The Challenges

When any market is in high-growth mode, planning around scalability and command and control are not top of the agenda. Serving customers and growth are the order of the day. But as we all know, light-speed growth comes at a cost of control and focus. Companies are now finding that traditional approaches to IT governance dashboards in the shift towards a digital-first economy are unable to cut the mustard. Governance dashboard are key in many regulated industries such as financial services to clearly demonstrate visibility and control. Specifically traditional IT governance starts to fall down against the new demands of a digital first economy because they:

Are not tied to business outcomes
Are overly dependent on a particular process or plan
Are unable to react to changing business priorities
Discourage open communication
Require a large manual overhead and maintenance

Do any of the above ring a bell? This is not about bashing tried and trusted approaches to technology governance, but about recognising continuous evaluation is required to keep pace with the changing face of technology and business.

The three-building blocks of modern technology governance

With the worlds of Dev, Ops and Sec all merging – cross functional teams are starting to come more mainstream. This can lead to over-engineering of new approaches ending up with even longer acronyms. I’m not looking forward to the day where we have to say ‘BizDevTestOpsGov’. Thankfully we are not there yet and the more common-sense approach of ‘keep it simple’ is the way forward.

If we break down what is required to establish an effective and efficient governance for digital transformation, we come to three main points of focus:

Simplified governance structures –In a high-velocity environment the tendency is to overcomplicate governance structures and reports. Stabilizing a simple repeatable embedded governance dashboard with a focus on good behaviours will deliver better Much like the Toyota approach to quality management, the focus in on repeatable outcomes that take account of changing priorities and business goals. These are behaviourally driven not process driven.
Set clear goals and principles – Governance dashboard targets are often lost in the noise of business change programmers and neglected until there is a raging fire. By setting clear goals at an organisational and team level backed up with a clear set of guiding principles, organisations can clearly track governance and progress. This enables teams to remain flexible enough to react to changing business priorities, while not neglecting the fundamentals of control and good governance.
Automating governance dashboard goals, transparency, and accountability– being able to gather highly valuable data from your tool chains by combining data sets and applying machine learning means you are able to identify specific patterns within your data to alert you to issues. With our platform neuro we can automate specific insights to support governance KPI’s – for example: by combining and measuring issue density, length of high priority issue resolution time, state changes and level of code change we can automatically trigger insights from multiple machine learning models as well as automatically generating and scheduling your governance reporting against specific KPIs.

Conclusion

Companies, both large and small, are becoming more complex now more than ever. By focusing on simplifying, using the right technology with a drop of common sense, and using an adequate governance dashboard, oversight can both be agile and effective in the new post COVID-19 age.

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Neuro for Git and Cucumber Reports

webmaster — Wed, 17 Nov 2021 11:00:38 +0000

As neuro continues down its product roadmap, frequently adding more great features and integrations, the use cases proliferate. In this blog, I’m focusing on two recently introduced integrations: Git and cucumber reports.

Cucumber sandwiches anyone?!

The Cucumber Behaviour-Driven Development (BDD) tool continues to expand its presence in the market. The ability to write feature files in a format easily understandable to the business (using the Gherkin language) provides several benefits, not least that it makes it far simpler to verify with stakeholders that you’re delivering and then testing the right thing.

If you have your tests set up to run automatically in Cucumber, the integration with neuro means that at any time you can get cucumber reports to quickly check your execution status at an overall level, by build, feature or tag. The ability to measure this data over time allows you to identify whether certain features or tags are prone to higher failure rates. This integration also allows users to track the build duration over time to easily identify problematic builds and potentially any server issues.

Git jiggy with it

Add Git integration to the mix, and you can supercharge your neuro git and cucumber reports. Which developer has the most commits? What is the frequency of commits and the trend over time? How many lines were changed in the past week? Or just today? Which files change the most?

Strength in numbers

One of the key benefits of neuro is that you can pull through information from multiple sources into a single view. This unlocks genuinely useful insights.

EXAMPLE:

Let’s say the number of commits per week is typically 40-50, but you notice from your daily neuro report (which is automatically sent) that this week the number of commits already stands at 45 and you’re only halfway through the week. An adjacent chart indicates that one developer has performed the majority of those commits and other charts, in the same report but from the Cucumber integration, highlight that one feature in particular, and two tags, have resulted in a high number of failed tests.

This allows you to have a conversation with the developer, dig into the problems they’re having and draw up some options to mitigate, e.g. adding an increased number of pair programming sessions with another developer that is considered an expert for that feature or those tags, or create additional and more targeted unit tests that would capture such problems a little earlier.

We’ve also covered in previous blogs that neuro integrates with JIRA and multiple test management tools. Imagine one overarching dashboard pulling through key MI metrics across build, development, test and defects.

Viewing this information over time, the data from Git and Cucumber allows you to make informed decisions about how to improve, and then take action.

You might also be interesting in our blogs on jira reporting, xray reporting, or zephyr reporting.

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