We'll talk about Team Topologies and related topics.

Motivation: I have experienced a lot of challenges in both technical and social manners and tried to develop solutions to cope with them throughout all the testing activities that I was involved in. Eventually, I have wrapped them up to make a list of common pitfalls which can be encountered by any tester and some golden rules to successfully get rid of them and manage a test project. 

The most common problems are:

• In continuous testing environments, due to time pressure, sometimes Quality focus may be shifted. How can we ensure to stick to the correct mindset even under stress?
• While trying lots of approaches, how can we optimize the best way? How can we decide to go with it? How can we monitor the progress, trends and results of each single POC?
• Test Reliability & Test Smells
• Efficiency

Some fundamental solutions are:

• Being truly agile: Adapt new solutions quickly.
• Manage the progress: Be aware of what is going on by setting KPIs to track & monitor with tools like CloudWatch, Grafana
• Technical part: Automation principles for the sake of robustness. Solutions to reduce flaky tests & analysis effort & costs.
• Holistic testing!

Do bugs speak?

Yes, they do. People speak different languages like English, German, French, Chinese etc. But is communication to bugs possible? It is important to understand them, because they really tell us something. There is valuable information underlying the defects of a software, and information mining from defects promises for improvements in terms of quality, time, effort and cost.

Problem Definition

A comprehensive analysis on all created defects can provide precious insights about the product. For instance; if we notice that a bunch of defects heap together on a feature, we can conclude that the feature should be investigated and cured. Or we can make some observations about the severity or assignee of similar defects. Therefore, there are some potential patterns to be discovered under defects.

Wrap-up

Defect analysis is very important for QA people, and especially for QA managers. We utilize lots of aspects to get an idea about the product itself or our procedures. For instance while monitoring defect distribution across testing types, we will discuss how to get an idea about the quality of our testing approach. I.e whether we are applying all types in a balanced way. (functional, performance, documentation, etc.) Or over another graph, in which we track the gap between open defects and resolved defects, we will discuss what action items we can take when the gap widens. Finally, with ML assistance, we will see how we can reduce manual effort and cost.

Results & Conclusion

In this session, we discuss data mining from bugs and usage of ML in defect management. Objective of the study is:

• To present in which ways defects can be analyzed

• To present how ML can be used to make observations over defects

• To provide empirical information supporting (b)

弊社のDASTを用いたセキュリティテストは導入から日が立ち、セキュリティリスクにおいてもDXにおいてもあるべき姿からは程遠いものでした。

まだ道半ばですが、当時の現状から理想へのロードマップと、現状どこまでたどり着きどんなことを行ってきたかをご紹介いたします。