Promise Keynote

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- The document discusses the history and limitations of traditional software metrics approaches, noting a lack of commercial relevance and failure to meet the objective of quantitative risk assessment. - It proposes that causal Bayesian network models provide a better approach to using metrics to quantitatively predict and manage risks, allowing decision-making and intervention. - An example causal model is presented relating defects introduced and found to factors like complexity, testing effort, and design quality, with results showing actual versus predicted defects.

Technology Education

New Directions for Software Metrics Norman Fenton Agena Ltd and Queen Mary University of London PROMISE 20 May 2007

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Metrics History: Typical Approach What I really want to measure (Y) What I can measure (X) Y = f (X)

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Using metrics and fault data to predict quality 0 0 Post-release faults 10 20 30 40 80 120 160 Pre-release faults ?

Pre-release vs post-release faults: actual Post-release faults 0 10 20 30 0 40 80 120 160 Pre-release faults

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A Causal Model (Bayesian net) Residual Defects Testing Effort Problem complexity Defects found and fixed Defects Introduced Design process quality Operational defects Operational usage

https://intranet.dcs.qmul.ac.uk/courses/coursenotes/DCS235/

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With the rise of software systems ranging from personal assistance to the nation's facilities, software defects become more critical concerns as they can cost millions of dollar as well as impact human lives. Yet, at the breakneck pace of rapid software development settings (like DevOps paradigm), the Quality Assurance (QA) practices nowadays are still time-consuming. Continuous Analytics for Software Quality (i.e., defect prediction models) can help development teams prioritize their QA resources and chart better quality improvement plan to avoid pitfalls in the past that lead to future software defects. Due to the need of specialists to design and configure a large number of configurations (e.g., data quality, data preprocessing, classification techniques, interpretation techniques), a set of practical guidelines for developing accurate and interpretable defect models has not been well-developed. The ultimate goal of my research aims to (1) provide practical guidelines on how to develop accurate and interpretable defect models for non-specialists; (2) develop an intelligible defect model that offer suggestions how to improve both software quality and processes; and (3) integrate defect models into a real-world practice of rapid development cycles like CI/CD settings. My research project is expected to provide significant benefits including the reduction of software defects and operating costs, while accelerating development productivity for building software systems in many of Australia's critical domains such as Smart Cities and e-Health.

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Challenges to Effective Performance Testing in CI

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https://agiletestingdays.us/session/challenges-to-effective-performance-testing-in-ci/ Performance testing in CI is a must. If you are looking to invest effort and money in it, you’ll better take certain things into account from day one. How do you manage to run a performance test against each service of your system? And what about running almost every day, all the tests? It's essential to take into consideration important aspects from the beginning to get the most out of it. Yes, it's a big effort, but it’s worth it. We want to share our experience and lessons learned from working with different teams in charge of maintaining tests and the infrastructure for continuous integration focused on the performance testing tasks.

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An Empirical Comparison of Model Validation Techniques for Defect Prediction ...

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Software analytics focuses on analyzing and modeling a rich source of software data using well-established data analytics techniques in order to glean actionable insights for improving development practices, productivity, and software quality. However, if care is not taken when analyzing and modeling software data, the predictions and insights that are derived from analytical models may be inaccurate and unreliable. The goal of this hands-on tutorial is to guide participants on how to (1) analyze software data using statistical techniques like correlation analysis, hypothesis testing, effect size analysis, and multiple comparisons, (2) develop accurate, reliable, and reproducible analytical models, (3) interpret the models to uncover relationships and insights, and (4) discuss pitfalls associated with analytical techniques including hands-on examples with real software data. R will be the primary programming language. Code samples will be available in a public GitHub repository. Participants will do exercises via either RStudio or Jupyter Notebook through Binder.

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One of the most challenging problems that test managers face involves implementing effective, meaningful, and insightful test metrics. Data and measures are the foundation of true understanding, but the misuse of metrics causes confusion, bad decisions, and demotivation. Rex Black shares how to avoid these unfortunate situations by using metrics properly as part of your test management process. How can we measure our progress in testing a project? What can metrics tell us about the quality of the product? How can we measure the quality of the test process itself? Rex answers these questions, illustrated with case studies and real-life examples. Learn how to use test case metrics, coverage metrics, and defect metrics in ways that demonstrate status, quantify effectiveness, and support smart decision making. Exercises provide immediate opportunities for you to apply the techniques to your own testing metrics. Join Rex to jump-start a new testing metrics program or gain new ideas to improve your existing one.

Challenges of Executing AI

Dr. Umesh Rao.Hodeghatta

According to recent research report by Wall Street Journal, AI project failure rates near 50%, more than 53% terminates at proof of concept level and does not make it to production. Gartner report says that nearly 80% of the analytics projects are not delivering any business value. That means for every 10 projects, only 2 projects are useful to the organization. Let us pause here a moment, rather than looking at what makes AI projects to fail, let’s look at the challenges involved in AI projects and find a solution to overcome these challenges. AI projects are different from traditional software projects. Typical software projects, as shown in Figure 1, consist of well-defined software requirements, high level design, coding, unit testing, system testing, and deployment along with beta testing or field testing. Now, organizations are adopting Agile process instead of traditional V or waterfall model, but still steps mentioned are valid. However, AI and Machine Learning projects’ methodology is different from the above. Our experience working on many AI/ML projects has given us insights on some of the challenges of executing AI projects. Also, we are in regular touch with senior executives and thought leaders from different industries who understand the success formula. The following discussion is based on our practical experience and knowledge gained in the field. Successful execution of AI projects depends on the following factors: 1. Clearly aligned Business Expectations 2. Clarity on Terminologies 3. Meeting Data Requirements 4. Tools and Technology 5. Right Resources 6. Understanding Output Results 7. Project Planning and the Process

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Promise Keynote

1. New Directions for Software Metrics Norman Fenton Agena Ltd and Queen Mary University of London PROMISE 20 May 2007

3. Metrics History: Typical Approach What I really want to measure (Y) What I can measure (X) Y = f (X)

9. Regression models….?

10. Using metrics and fault data to predict quality 0 0 Post-release faults 10 20 30 40 80 120 160 Pre-release faults ?

11. Pre-release vs post-release faults: actual Post-release faults 0 10 20 30 0 40 80 120 160 Pre-release faults

12. What we need What I think is... ?

13.

14. A Causal Model (Bayesian net) Residual Defects Testing Effort Problem complexity Defects found and fixed Defects Introduced Design process quality Operational defects Operational usage

15. A Model in action

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17. https://intranet.dcs.qmul.ac.uk/courses/coursenotes/DCS235/

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29. Actual versus predicted defects

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Editor's Notes

Joke about Savoy. Who I am – show my book. I’ll have something more to say about this book shortly. One of the best books on software metrics was written by Bob Grady of Hewlett Packard in 1987. Bob was responsible for what I believe was recognised as the first true company-wide metrics programme. And his book described the techniques and experiences associated with that at HP. A few years later I was at a meeting where Bob told an interesting story about that metrics programme. He said that one of the main objectives of the programme was to achieve process improvement by learning from metrics what process activities worked and what ones didn’t. To do this they looked at those projects that in metrics terms were considered most successful. These were the projects with especially low rates of customer-reported defects. The idea was to learn what processes characterised such successful projects. It turned out that what they learned from this was very different to what they had expected. I am not going to tell you what it was they learnt until the end of my presentation; by then you may have worked it out for yourselves.

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Editor's Notes