A Quality Model for Actionable Analytics in Rapid Software Development
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Background: Accessing relevant data on the software product, process, and usage as well as integrating and analysing it is crucial to get reliable and timely actionable insights for continuously managing software quality in Rapid Software Development (RSD). In this context, several software analytics tools have been developed in recent years. However, there is a lack of explainable software analytics that software practitioners trust. Aims: We aimed at creating a quality model -the Q-Rapids quality model- for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies for determining relevant metrics as well as product and process factors. We also elicited how these metrics and factors are used and interpreted by practitioners when making decisions in RSD. We specified the Q-Rapids quality model by comparing and integrating the four workshops' results. Then, we implemented the Q-Rapids tool for supporting the usage of the Q-Rapids quality model as well as the gathering, integration, and analysis of the required data. Afterwards we installed the Q-Rapids tool in the four companies and performed semi-structured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceive the metrics as well as product and process factors of the Q-Rapids quality model as understandable. Also, they consider the Q-Rapids quality model relevant for identifying product and process deficiencies (e.g., blocking code situations). Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model is an enabler for detecting manually time-consuming problems and adding transparency among the system, process, and usage perspectives.
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