How do we make decisions regarding testing and quality assurance in our day-to-day profession as quality engineers? This is a key question in order to understand the importance of data. With no data, there is no information to make decisions with more criteria than intuition. This is why Data-Driven Decision Management is a popular and widely used approach for taking any decision. This is the same that we need for making intelligent decisions in the field of quality assurance, that also requires continuous prioritization and decisions.
It is well known that any testing and QA implementation is aimed, in first stages, at structuring testing and quality evidences and activities in repositories and tools, together with a suitable testing process. It is important to note, at this point, that we create data regarding quality (test case designs, test case executions, defects, requirements, etc.) and we usually save them explicitly. When this data is also related to traceability, then this information has great value further than operative usage. Then, it’s time to follow the journey and make progress to business intelligence, as a way to support decisions through Key Performance Indicators (KPIs), which may be continuously improved with more data (e.g. development commits, project management data, support cases, software reviews, etc.) that may influence the decisions about quality assurance and make them more robust. In order to this, it is important to automatically collect data and store them into unified databases. The QADashboards solution developed by our Lab in Spain is an interesting asset for fulfilling this objective.
Many times, we can become surprised when using data further than its original operative purpose. Moreover, thinking about the journey of data usage is a good way to decide which data fields need to be collected at operative level, by applying purpose-driven data collection instead of bureaucracy.
Finally, when operative data is used at decision management level, a next level in the journey can be addressed: predictions through cognitive models, that may trigger automated actions depending on the best-predicted alternative. Sogeti’s CognitiveQA is the asset to reach the top stages of an effective use of our data. Otherwise, if we are not pushing for going forward in the journey, we will be clearly misusing the power of data that we already have and losing the opportunities for better-informed decisions.
As Arthur C. Nielsen said, “The price of light is less than the cost of darkness.” So… let’s follow the journey!