In despite of agile and rapid software development (ARSD) being researched and applied extensively, managing quality requirements (QRs) are still challenging. As ARSD processes produce a large amount of data, measurement has become a strategy to facilitate QR management. This study aims to survey the literature related to QR management through metrics in ARSD, focusing on: bibliometrics, QR metrics, and quality-related indicators used in quality management. The study design includes the definition of research questions, selection criteria, and snowballing as search strategy. We selected 61 primary studies (2001–2019). Despite a large body of knowledge and standards, there is no consensus regarding QR measurement. Terminology is varying as are the measuring models. However, seemingly different measurement models do contain similarities. The industrial relevance of the primary studies shows that practitioners have a need to improve quality measurement. Our collection of measures and data sources can serve as a starting point for practitioners to include quality measurement into their decision-making processes. Researchers could benefit from the identified similarities to start building a common framework for quality measurement. In addition, this could help researchers identify what quality aspects need more focus, e.g., security and usability that have surprisingly few metrics reported.

AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.

Background: Despite the growth in the use of software analytics platforms in industry, little empirical evidence is available about the challenges that practitioners face and the value that these platforms provide. Aim: The goal of this research is to explore the benefits of using a software analytics platform for practitioners managing quality. Method: In a technology transfer project, a software analytics platform was incrementally developed between academic and industrial partners to address their software quality problems. This paper focuses on exploring the value provided by this software analytics platform in two pilot projects. Results: Practitioners emphasized major benefits including the improvement of product quality and process performance and an increased awareness of product readiness. They especially perceived the semi-automated functionality of generating quality requirements by the software analytics platform as the benefit with the highest impact and most novel value for them. Conclusions: Practitioners can benefit from modern software analytics platforms, especially if they have time to adopt such a platform carefully and integrate it into their quality assurance activities.

Abstract In agile software development, functionality is added to the system in an incremental and iterative manner. Practitioners often rely on expert judgment to estimate the effort in this context. However, the impact of a change on the existing system can provide objective information to practitioners to arrive at an informed estimate. In this regard, we have developed a hybrid method, that utilizes change impact analysis information for improving effort estimation. We also developed an estimation model based on gradient boosted trees (GBT). In this study, we evaluate the performance and usefulness of our hybrid method with tool support and the GBT model in a live iteration at Insiders Technologies GmbH, a German software company. Additionally, the solution was also assessed for perceived usefulness and understandability in a study with graduate and post‐graduate students. The results from the industrial evaluation show that the proposed method produces more accurate estimates than only expert‐based or only model‐based estimates. Furthermore, both students and practitioners perceived the usefulness and understandability of the method positively.

Existing definitions and metrics of technical debt (TD) tend to focus on static properties of software artifacts, in particular on code measurement. Our experience from software renovation projects is that dynamic aspects - runtime indicators of TD - often play a major role. In this position paper, we present insights and solution ideas gained from numerous software renovation projects at QAware and from a series of interviews held as part of the ProDebt research project. We interviewed ten practitioners from two German software companies in order to understand current requirements and potential solutions to current problems regarding TD. Based on the interview results, we motivate the need for measuring dynamic indicators of TD from the practitioners' perspective, including current practical challenges. We found that the main challenges include a lack of production-ready measurement tools for runtime indicators, the definition of proper metrics and their thresholds, as well as the interpretation of these metrics in order to understand the actual debts and derive countermeasures. Measuring and interpreting dynamic indicators of TD is especially difficult to implement for companies because the related metrics are highly dependent on runtime context and thus difficult to generalize. We also sketch initial solution ideas by presenting examples of dynamic indicators for TD and outline directions for future work.

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 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 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.

In agile software development, functionality is added to the system in an incremental and iterative manner. Practitioners often rely on expert-judgment to estimate effort in this context. The impact of a change on the existing system can provide objective information to practitioners to arrive at an informed estimate. In this regard, we have developed an innovative hybrid method that utilizes change impact analysis information for improving effort estimation. Additionally, an estimation model based on boosted trees is also developed. In this study, we evaluate the performance and usefulness of our innovative method and model in the context of agile software development from the perspective of agile development teams. A case study has been conducted with Insiders Technologies, a German software company, where we have applied our proposed method with tool-support in a live iteration and have evaluated it. The results show that the proposed method is useful and effective. It produces more accurate estimates than purely expert-based or purely model-based estimates.

Context/Background: To achieve high software quality, particularly in the context of agile software development, organizations need tools to continuously analyze software quality. Several quality management (QM) tools have been developed in recent years. However, there is a lack of evidence regarding the quality of QM tools, standardized definitions of such quality, and reliable instruments for measuring it. This, in turn, impedes proper selection and improvement of QM tools. Goals: We aimed at operationalizing the quality of a research QM tool, namely the ProDebt prototype, and evaluating its quality. The goal of the ProDebt prototype is to provide practitioners with support for managing software quality and technical debt. Method: We performed interviews, workshops, and a mapping study to operationalize the quality of the ProDebt prototype and to identify reliable instruments to measure it. We designed a mixed-method study aimed at formative evaluation, i.e., at assessing the quality of the ProDebt prototype and providing guidance for its further development. Eleven practitioners from two German companies evaluated the ProDebt prototype. Results: The participants assessed the information provided by the ProDebt prototype as understandable and relevant. They considered the ProDebt prototype's functionalities as easy to use but of limited usability. They identified improvement needs, e.g., that the analysis results should be linked to other information sources. Conclusions: The evaluation design was of practical value for evaluating the ProDebt prototype considering the limited resources such as the practitioners' time. The evaluation results provided the developers of the ProDebt prototype with guidance for its further development. We conclude that it can be used and tailored for replication or evaluation of other QM tools.

Constantly evolving requirements and use of expert judgment makes effort estimation challenging in the Agile development context. To improve expert judgment based estimation (EE) methods, we introduced a framework to integrate these with change impact analysis (IA) techniques. In this paper, we report the findings of an empirical investigation which we performed together at SAP SE, a German multinational software corporation. The objective of this study was to evaluate the concept of utilizing IA for EE. Furthermore, through mock-up we identified and further refined the workflow, and evaluated its usefulness from the perspective of agile development teams. The results indicate that the overall concept of utilizing change impact analysis is perceived as very useful for supporting effort estimation. The workflow instantiated via mock-up was understandable, easy to use, provides useful visualization capabilities and practitioners expressed interest to use it in their estimation process.