Q-Rapids is still producing papers

After two years since the Q-Rapids project (H2020) finised on October 2019, we finally get published one of the systematic mappings conducted during the project. We have now published our study on quality related metrics and indicators for monitoring Agile development processes.

Quality measurement in agile and rapid software development: A systematic mapping

Lidia López, Xavier Burgués, Silverio Martínez-Fernández, Anna Maria Vollmer, Woubshet Behutiye, Pertti Karhapää, Xavier Franch, Pilar Rodríguez, Markku Oivo

Abstract. Context: 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. Objective: 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. Methods: The study design includes the definition of research questions, selection criteria, and snowballing as search strategy. Results: 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. Conclusion: 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

 

Link to the Open Access paper

Paper accepted at the 13th International Symposium on Empirical Software Engineering and Measurement (ESEM 2019)

Practical experiences and value of applying software analytics for managing quality

Anna Maria Vollmer, Silverio Martínez-Fernández, Alessandra Bagnato, Jari Partanen, Pilar Rodriguez Gonzalez and Lidia Lopez

Abstract. Background: Despite the growth of usage of software analytics platforms in industry, little empirical evidence is available about the challenges that practitioners face and the value that these platforms give. Aim: The goal of this research is to explore the benefits from 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 platform was used in two pilot projects. This paper focuses on exploring the value provided by a software analytics platform in these pilot projects. Results: Practitioners have emphasized main benefits including the semiautomated functionality of creating quality requirements, the improvement of product quality and process performance, and an

increased awareness of product readiness. They have especially perceived the semi-automated functionality of creating quality requirements out of the software analytics platform as the

benefit with the highest impact and most novel value for them. Conclusions: Practice can benefit from modern software analytics platforms, especially if they have time to adopt it carefully and

integrate it into their quality assurance activities.

Paper published in the IEEE Access journal (JCR 201: 4.098 - Q1)

Continuously assessing and improving software quality with software analytics tools: a case study

Silverio Martínez-Fernández, Anna Maria Vollmer, Andreas Jedlitschka, Xavier Franch, Lidia López, Prabhat Ram, Pilar Rodríguez, Sanja Aaramaa, Alessandra Bagnato, Michał Choraś and Jari Partanen 

Abstract. In the last decade, modern data analytics technologies have enabled the creation of software analytics tools offering real-time visualization of various aspects related to software development and usage. These tools seem to be particularly attractive for companies doing agile software development. However, the information provided by the available tools is neither aggregated nor connected to higher quality goals. At the same time, assessing and improving software quality has also been a key target for the software engineering community, yielding several proposals for standards and software quality models. Integrating such quality models into software analytics tools could close the gap by providing the connection to higher quality goals. This study aims at understanding whether the integration of quality models into software analytics tools provides understandable, reliable, useful, and relevant information at the right level of detail about the quality of a process or product, and whether practitioners intend to use it. Over the course of more than one year, the four companies involved in this case study deployed such a tool to assess and improve software quality in several projects. We used standardized measurement instruments to elicit the perception of 22 practitioners regarding their use of the tool. We complemented the findings with debriefing sessions held at the companies. In addition, we discussed challenges and lessons learned with four practitioners leading the use of the tool. Quantitative and qualitative analyses provided positive results; i.e., the practitioners’ perception with regard to the tool’s understandability, reliability, usefulness, and relevance was positive. Individual statements support the statistical findings and constructive feedback can be used for future improvements. We conclude that potential for future adoption of quality models within software analytics tools definitely exists and encourage other practitioners to use the presented seven challenges and seven lessons learned and adopt them in their companies.

 

Link to the Open Access paper