Continuously Assessing and Improving Software Quality With Software Analytics Tools: A Case Study

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

doi:10.1109/access.2019.2917403

Cited by 53 publications

(53 citation statements)

References 43 publications

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“…The alignment of the company's software development process and the company tool chains together with the configuration of Q-Rapids has been an important factor in building a successful implementation of Q-Rapids. The first implementation of Q-Rapids took place in 2018 and it has been up and running for nearly one year [3]. The second pilot consists of a product family with five products and related components in the context of product information systems development.…”

Section: B Company Amentioning

confidence: 99%

“…Software analytics is about utilizing data-driven approaches to obtain insightful and actionable information to help software practitioners with their data-related tasks [1]. Software analytics platforms provide features for analyzing and visualizing software development data to support data-driven decisionmaking [2], [3]. Despite the growth in the use of software analytics platforms in industry, little empirical evidence is available about the extra value they provide [4].…”

Section: Introductionmentioning

confidence: 99%

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Practical experiences and value of applying software analytics to manage quality

Vollmer

Martínez‐Fernández

Bagnato

et al. 2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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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.

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Section: B Company Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Practical experiences and value of applying software analytics to manage quality

Vollmer

Martínez‐Fernández

Bagnato

et al. 2019

2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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“…The platform relies on a quality model (QM) to define three abstraction levels: key strategic indicators, project and process factors, and metrics [3]. The key strategic indicators can be assessed qualitatively defining a Bayesian Network based on the factors impacting on them [4]. • Predicting future quality levels through multiple predictive methods [5].…”

Section: Achieved Outcomesmentioning

confidence: 99%

Q-Rapids: Quality-Aware Rapid Software Development – An H2020 Project

López

Oriol

2019

Product-Focused Software Process Improvement

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This work reports the objectives, current state, and outcomes of the Q-Rapids H2020 project. Q-Rapids (Quality-Aware Rapid Software Development) proposes a data-driven approach to the production of software following very short development cycles. The focus of Q-Rapids is on quality aspects, represented through quality requirements. The Q-Rapids platform, which is the tangible software asset emerging from the project, mines software repositories and usage logs to identify candidate quality requirements that may ameliorate the values of strategic indicators like product quality, time to market or team productivity. Four companies are providing use cases to evaluate the platform and associated processes.

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“…Practitioners need to investigate software runtime quality problems during the early software development stages, for instance, for guiding refactoring activities [17]. In this context, decisions are also made more frequently and the use of software analytics is particularly attractive [13].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have reported that the use of data analytics is particularly attractive for software companies [6,13]. Indeed, companies like Microsoft are hiring data scientists for their software teams [8].…”

Section: Introductionmentioning

confidence: 99%

Integrating runtime data with development data to monitor external quality: challenges from practice

Aghabayli

Pfahl

Martínez‐Fernández

et al. 2019

Proceedings of the 2nd ACM SIGSOFT International Workshop on Software Qualities and Their Dependencies

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The use of software analytics in software development companies has grown in the last years. Still, there is little support for such companies to obtain integrated insightful and actionable information at the right time. This research aims at exploring the integration of runtime and development data to analyze to what extent external quality is related to internal quality based on real project data. Over the course of more than three months, we collected and analyzed data of a software product following the CRISP-DM process. We studied the integration possibilities between runtime and development data, and implemented two integrations. The number of bugs found in code has a weak positive correlation with code quality measures and a moderate negative correlation with the number of rule violations found. Other types of correlations require more data cleaning and higher quality data for their exploration. During our study, several challenges to exploit data gathered both at runtime and during development were encountered. Lessons learned from integrating external and internal data in software projects may be useful for practitioners and researchers alike.

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Continuously Assessing and Improving Software Quality With Software Analytics Tools: A Case Study

Cited by 53 publications

References 43 publications

Practical experiences and value of applying software analytics to manage quality

Practical experiences and value of applying software analytics to manage quality

Q-Rapids: Quality-Aware Rapid Software Development – An H2020 Project

Integrating runtime data with development data to monitor external quality: challenges from practice

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