A Quality Model for Actionable Analytics in Rapid Software Development

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.Comment: This is an Author's Accepted Manuscript of a paper to be published by IEEE in the 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2018. The final authenticated version will be available onlin

Applying project-based learning to teach software analytics and best practices in data science

Due to recent industry needs, synergies between data science and software engineering are starting to be present in data science and engineering academic programs. Two synergies are: applying data science to manage the quality of the software (software analytics) and applying software engineering best practices in data science projects to ensure quality attributes such as maintainability and reproducibility. The lack of these synergies on academic programs have been argued to be an educational problem. Hence, it becomes necessary to explore how to teach software analytics and software engineering best practices in data science programs. In this context, we provide hands-on for conducting laboratories applying project-based learning in order to teach software analytics and software engineering best practices to data science students. We aim at improving the software engineering skills of data science students in order to produce software of higher quality by software analytics. We focus in two skills: following a process and software engineering best practices. We apply project-based learning as main teaching methodology to reach the intended outcomes. This teaching experience shows the introduction of project-based learning in a laboratory, where students applied data science and best software engineering practices to analyze and detect improvements in software quality. We carried out a case study in two academic semesters with 63 data science bachelor students. The students found the synergies of the project positive for their learning. In the project, they highlighted both utility of using a CRISP-DM data mining process and best software engineering practices like a software project structure convention applied to a data science project.This paper was partly funded by a teaching innovation project of ICE@UPC-BarcelonaTech (entitled ‘‘Audiovisual and digital material for data engineering, a teaching innovation project with open science’’), and the ‘‘Beatriz Galindo’’ Spanish Program BEA-GAL18/00064.Peer ReviewedPostprint (published version

A quality model for actionable analytics in rapid software development

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.Peer ReviewedPostprint (author's final draft

Aprendizaje basado en proyectos de analítica de software en estudios de ciencia e ingeniería de datos

El Grado en Ciencia e Ingeniería de Datos (GCED) de la Universidad Politécnica de Cataluña (UPC) forma a sus estudiantes en análisis e ingeniería de datos con una base matemática y habilidades propias de la ingeniería que les permiten modelizar y resolver problemas complejos. En el último curso, los estudiantes aún no han analizado conjuntos de datos que permitan ayudar a mejorar la calidad del software, ni explotado buenas prácticas de ingeniería del software en proyectos de ciencias de datos. En el curso 2020/2021 se cursa por primera vez la asignatura Temas Avanzados en Ingeniería de Datos 2 (TAED2) en el GCED. Esta experiencia docente relata la introducción del aprendizaje basado en proyectos en el laboratorio de la asignatura, donde los estudiantes aplicaron la ciencia de datos y buenas prácticas de ingeniería del software para analizar y detectar mejoras en la calidad del software. Realizamos un caso de estudio en siete sesiones de laboratorio con 33 estudiantes del GCED. Los estudiantes encontraron la realización del proyecto positiva para su aprendizaje. En el proyecto, destacaron la utilidad de usar un proceso de minería de datos CRISP-DM y una buena práctica de ingeniería del software: una convención de estructura de un proyecto software aplicada a un proyecto de ciencia de datos.The Bachelor's Degree in Data Science and Engineering (GCED) at UPC-BarcelonaTech trains its students in data analysis and engineering with a mathematical basis and engineering skills that allow them to model and solve complex problems. In the last year, students have not yet analyzed data sets that help improving software quality, nor have they exploited good software engineering practices in data science projects. In the 2020/2021 academic year, the subject Advanced Topics in Data Engineering 2 (TAED2) is taken at the GCED for the first time. This teaching experience shows the introduction of project-based learning in the subject laboratory, where students applied data science and good software engineering practices to analyze and detect improvements in software quality. We carried out a case study in seven laboratory sessions with 33 GCED students. The students found the execution of the project positive for their learning. In the project, they highlighted the utility of using a CRISP-DM data mining process and good software engineering practice: a software project structure convention applied to a data science project.Esta experiencia docente está parcialmente financiada por el programa “Beatriz Galindo”

Data mining for software engineering and humans in the loop

The field of data mining for software engineering has been growing over the last decade. This field is concerned with the use of data mining to provide useful insights into how to improve software engineering processes and software itself, supporting decision-making. For that, data produced by software engineering processes and products during and after software development are used. Despite promising results, there is frequently a lack of discussion on the role of software engineering practitioners amidst the data mining approaches. This makes adoption of data mining by software engineering practitioners difficult. Moreover, the fact that experts’ knowledge is frequently ignored by data mining approaches, together with the lack of transparency of such approaches, can hinder the acceptability of data mining by software engineering practitioners. To overcome these problems, this position paper provides a discussion of the role of software engineering experts when adopting data mining approaches. It also argues that this role can be extended to increase experts’ involvement in the process of building data mining models. We believe that such extended involvement is not only likely to increase software engineers’ acceptability of the resulting models, but also improve the models themselves. We also provide some recommendations aimed at increasing the success of experts involvement and model acceptability

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

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.Peer ReviewedPostprint (published version

Software Development Analytics (Dagstuhl Seminar 14261)

This report documents the program and the outcomes of Dagstuhl Seminar 14261 "Software Development Analytics". We briefly summarize the goals and format of the seminar, the results of the break out groups, and a draft of a manifesto for software analytics. The report also includes the abstracts of the talks presented at the seminar