ABSTRACT
Context Software engineering activities provide practitioners with large volumes of data that software analytics tools can use for many purposes, including defect prediction and effort estimation. However, the adoption of such tools depends on the information they provide and the real needs of practitioners. While existing research has focused on what developers need, the needs of managers are not well understood. Aims This study provides an in-depth analysis of the information needs of software practitioners from one organization that performs research, development, and innovation projects with industry partners. Understanding these practitioners’ needs enables the development of better analytics solutions to support managerial decision-making. Method We interviewed practitioners in leadership positions and analyzed the collected data using Grounded Theory coding techniques, i.e., open and selective coding. Results We identified 19 software analytics use cases and classified them into four dimensions: quality, people, project management, and knowledge management. We also elicited several indicators to meet the identified use cases and captured key aspects concerning the organization’s analytics scenario. Conclusions Although our results are particularly relevant to organizations similar to the one in which we conducted the study, they aim to serve as input for implementing new analytics solutions by practitioners and researchers in general.
Supplemental Material
Available for Download
- Abdullah Al-Nayeem, Krzysztof Ostrowski, Sebastian Pueblas, Christophe Restif, and Sai Zhang. 2017. Information needs for validating evolving software systems: An exploratory study at google. In 2017 IEEE International Conference on Software Testing, Verification and Validation (ICST). IEEE, 544–545.Google ScholarCross Ref
- Andrew Begel and Thomas Zimmermann. 2014. Analyze this! 145 questions for data scientists in software engineering. In Proceedings of the 36th International Conference on Software Engineering. 12–23.Google ScholarDigital Library
- Jacob T Biehl, Mary Czerwinski, Greg Smith, and George G Robertson. 2007. FASTDash: a visual dashboard for fostering awareness in software teams. In Proceedings of the SIGCHI conference on Human factors in computing systems. 1313–1322.Google ScholarDigital Library
- Katarzyna Biesialska, Xavier Franch, and Victor Muntés-Mulero. 2021. Big Data analytics in Agile software development: A systematic mapping study. Information and Software Technology 132 (2021), 106448.Google ScholarCross Ref
- Lionel Briand, Domenico Bianculli, Shiva Nejati, Fabrizio Pastore, and Mehrdad Sabetzadeh. 2017. The case for context-driven software engineering research: generalizability is overrated. IEEE Software 34, 5 (2017), 72–75.Google ScholarDigital Library
- Raymond PL Buse and Thomas Zimmermann. 2010. Analytics for software development. In Proceedings of the FSE/SDP workshop on Future of software engineering research. 77–80.Google ScholarDigital Library
- Raymond PL Buse and Thomas Zimmermann. 2012. Information needs for software development analytics. In 2012 34th International Conference on Software Engineering (ICSE). IEEE, 987–996.Google ScholarCross Ref
- Joelma Choma, Eduardo Martins Guerra, and Tiago Silva Da Silva. 2017. Patterns for implementing software analytics in development teams. In Proceedings of the 24th Conference on Pattern Languages of Programs. 1–12.Google ScholarDigital Library
- Iris Figalist, Christoph Elsner, Jan Bosch, and Helena Holmström Olsson. 2020. Breaking the Vicious Circle: Why AI for software analytics and business intelligence does not take off in practice. In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 5–12.Google ScholarCross Ref
- Latifa Guerrouj, Olga Baysal, David Lo, and Foutse Khomh. 2016. Software analytics: challenges and opportunities. In 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C). IEEE, 902–903.Google ScholarDigital Library
- Mohammad Ibraigheeth and Syed Abdullah Fadzli. 2019. Core factors for software projects success. JOIV: International Journal on Informatics Visualization 3, 1(2019), 69–74.Google ScholarCross Ref
- Silverio Martínez-Fernández, Anna Maria Vollmer, Andreas Jedlitschka, Xavier Franch, Lidia López, Prabhat Ram, Pilar Rodríguez, Sanja Aaramaa, Alessandra Bagnato, Michał Choraś, 2019. Continuously assessing and improving software quality with software analytics tools: a case study. IEEE access 7(2019), 68219–68239.Google ScholarCross Ref
- Mohd Hairul Nizam Nasir and Shamsul Sahibuddin. 2011. Critical success factors for software projects: A comparative study. Scientific research and essays 6, 10 (2011), 2174–2186.Google Scholar
- Ali Nizam. 2022. Software Project Failure Process Definition. IEEE Access (2022).Google ScholarCross Ref
- Luca Pascarella, Davide Spadini, Fabio Palomba, Magiel Bruntink, and Alberto Bacchelli. 2018. Information needs in contemporary code review. Proceedings of the ACM on Human-Computer Interaction 2, CSCW(2018), 1–27.Google ScholarDigital Library
- Mirko Perkusich, Lenardo Chaves e Silva, Alexandre Costa, Felipe Ramos, Renata Saraiva, Arthur Freire, Ednaldo Dilorenzo, Emanuel Dantas, Danilo Santos, Kyller Gorgônio, 2020. Intelligent software engineering in the context of agile software development: A systematic literature review. Information and Software Technology 119 (2020), 106241.Google ScholarDigital Library
- Kai Petersen and Claes Wohlin. 2009. Context in industrial software engineering research. In 2009 3rd International Symposium on Empirical Software Engineering and Measurement. IEEE, 401–404.Google ScholarDigital Library
- Shaun Phillips, Guenther Ruhe, and Jonathan Sillito. 2012. Information needs for integration decisions in the release process of large-scale parallel development. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. 1371–1380.Google ScholarDigital Library
- Pilar Rodríguez, Emilia Mendes, and Burak Turhan. 2018. Key stakeholders’ value propositions for feature selection in software-intensive products: An industrial case study. IEEE Transactions on Software Engineering 46, 12 (2018), 1340–1363.Google ScholarCross Ref
- Per Runeson and Martin Höst. 2009. Guidelines for conducting and reporting case study research in software engineering. Empirical software engineering 14, 2 (2009), 131–164.Google Scholar
- Mali Senapathi, Jim Buchan, and Hady Osman. 2018. DevOps capabilities, practices, and challenges: Insights from a case study. In Proceedings of the 22nd International Conference on Evaluation and Assessment in Software Engineering 2018. 57–67.Google ScholarDigital Library
- Mojtaba Shahin and M Ali Babar. 2020. On the role of software architecture in DevOps Transformation: An industrial case study. In Proceedings of the International Conference on Software and System Processes. 175–184.Google ScholarDigital Library
- Miroslaw Staron and Wilhelm Meding. 2018. Software Development Measurement Programs. Springer. https://doi. org/10.1007/978-3-319-91836-5 10 (2018), 3281333.Google Scholar
- Christoph Treude, Fernando Figueira Filho, and Uirá Kulesza. 2015. Summarizing and measuring development activity. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. 625–636.Google ScholarDigital Library
- Cathy Urquhart. 2012. Grounded theory for qualitative research: A practical guide. Sage.Google Scholar
- Cathy Urquhart and Walter Fernández. 2016. Using grounded theory method in information systems: The researcher as blank slate and other myths. In Enacting research methods in information systems: Volume 1. Springer, 129–156.Google Scholar
- Claes Wohlin and Aybüke Aurum. 2015. Towards a decision-making structure for selecting a research design in empirical software engineering. Empirical Software Engineering 20, 6 (2015), 1427–1455.Google ScholarDigital Library
- Dongmei Zhang, Shi Han, Yingnong Dang, Jian-Guang Lou, Haidong Zhang, and Tao Xie. 2013. Software analytics in practice. IEEE software 30, 5 (2013), 30–37.Google ScholarDigital Library
Index Terms
Use Cases for Software Development Analytics: A Case Study
Recommendations
Patterns for implementing software analytics in development teams
PLoP '17: Proceedings of the 24th Conference on Pattern Languages of ProgramsThe software development activities typically produce a large amount of data. Using a data-driven approach to decision making - such as Software Analytics - the software practitioners can achieve higher development process productivity and improve many ...
Streaming software analytics
BIGDSE '16: Proceedings of the 2nd International Workshop on BIG Data Software EngineeringIn this paper we present a novel software analytics infrastructure supporting for a combination of three requirements to serve software practitioners in utilising data-driven decision making: (1) Real-time insight: streaming software analytics unify ...
Software Analytics in Practice
With software analytics, software practitioners explore and analyze data to obtain insightful, actionable information for tasks regarding software development, systems, and users. The StackMine project produced a software analytics system for Microsoft ...
Comments