Background: Accessing relevant data on the product, process, and usage perspectives of software as well as integrating and analyzing such data is crucial for getting reliable and timely actionable insights aimed at 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 (called Q-Rapids quality model) for actionable analytics in RSD, implementing it, and evaluating its understandability and relevance. Method: We performed workshops at four companies in order to determine 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 results of the four workshops. Then we implemented the Q-Rapids tool to support 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 semistructured interviews with eight product owners to evaluate the understandability and relevance of the Q-Rapids quality model. Results: The participants of the evaluation perceived the metrics as well as the product and process factors of the Q-Rapids quality model as understandable. Also, they considered 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 enables detecting problems that take more time to find manually and adds transparency among the perspectives of system, process, and usage.

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Context: Rapid software development (RSD) refers to the organizational capability to develop, release, and learn from software in rapid cycles without compromising its quality. To achieve RSD, it is essential to understand and manage software quality along the software lifecycle. Problem: Despite the numerous information sources related to product quality, there is a lack of mechanisms for supporting continuous quality management throughout the whole RSD process. Principal ideas/results: We propose Q-Rapids, a data-driven, qualityaware RSD methodology in which quality and functional requirements are managed together. Quality Requirements are incrementally elicited and refined based on data gathered at both development time and runtime. Project, development, and runtime data is aggregated into quality-related key indicators to support decision makers in steering future development cycles. Contributions: Q-Rapids aims to increase software quality through continuous data gathering and analysis, as well as continuous management of quality requirements.

Effort estimation is more challenging in an agile context, as instead of exerting strict control over changes in requirements, dynamism is embraced. Current practice relies on expert judgment, where the accuracy of estimates is sensitive to the expertise of practitioners and prone to bias. To improve the effectiveness of the effort estimation process, the goal of this research is to investigate and understand the estimation process with respect to its accuracy in the context of agile software development from the perspective of agile development teams. Using case study research, 2 observations and eleven interviews were conducted with 3 agile development teams at SAP SE, a German multinational software corporation. The results reveal that factors such as the developer's knowledge, experience, and the complexity and impact of changes on the underlying system affect the magnitude as well as estimation accuracy. Furthermore, there is a need for a tool that incorporates expert knowledge, enables explicit consideration of cost drivers by experts and visualizes this information to improve the effectiveness of the effort estimation. On the basis of the findings of the case study, a framework, inspired by the quality improvement paradigm is proposed to improve effort estimation in agile development

Effort estimation is more challenging in an agile context, as instead of exerting strict control over changes in requirements, dynamism is embraced. Current practice relies on expert judgment, where the accuracy of estimates is sensitive to the expertise of practitioners and prone to bias. In order to improve the effectiveness of the effort estimation process, the goal of this research is to investigate and understand the estimation process with respect to its accuracy in the context of agile software development from the perspective of agile development teams. Using case study research, two observations and eleven interviews were conducted with three agile development teams at SAP SE, a German multinational software corporation. The results reveal that factors such as the developer's knowledge and experience and the complexity and impact of changes on the underlying system affect the magnitude as well as the accuracy of estimation. Moreover, if certain aspects of the estimation process, such as the potential impact of a change on the underlying system, are supported by a tool can help improve estimation accuracy. We conclude that explicit consideration of these factors in the estimation process can support experts in making accurate and informed estimates. Furthermore, there is a need for a tool that incorporates expert knowledge, enables explicit consideration of cost drivers by experts and visualizes this information in order to improve the effectiveness of the effort estimation process.