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Background: Accessing relevant data on the product, process, and usageperspectives of software as well as integrating and analyzing such data iscrucial for getting reliable and timely actionable insights aimed atcontinuously managing software quality in Rapid Software Development (RSD). Inthis context, several software analytics tools have been developed in recentyears. However, there is a lack of explainable software analytics that softwarepractitioners trust. Aims: We aimed at creating a quality model (calledQ-Rapids quality model) for actionable analytics in RSD, implementing it, andevaluating its understandability and relevance. Method: We performed workshopsat four companies in order to determine relevant metrics as well as product andprocess factors. We also elicited how these metrics and factors are used andinterpreted by practitioners when making decisions in RSD. We specified theQ-Rapids quality model by comparing and integrating the results of the fourworkshops. Then we implemented the Q-Rapids tool to support the usage of theQ-Rapids quality model as well as the gathering, integration, and analysis ofthe required data. Afterwards we installed the Q-Rapids tool in the fourcompanies and performed semi-structured interviews with eight product owners toevaluate the understandability and relevance of the Q-Rapids quality model.Results: The participants of the evaluation perceived the metrics as well asthe product and process factors of the Q-Rapids quality model asunderstandable. Also, they considered the Q-Rapids quality model relevant foridentifying product and process deficiencies (e.g., blocking code situations).Conclusions: By means of heterogeneous data sources, the Q-Rapids quality modelenables detecting problems that take more time to find manually and addstransparency among the perspectives of system, process, and usage.

A Quality Model for Actionable Analytics in Rapid Software Development