Mining Data from Software Repositories

2016 ◽  
pp. 169-232
Keyword(s):  
Software Repositories

scholarly journals Individual differences limit predicting well-being and productivity using software repositories: a longitudinal industrial study

2021 ◽  
Vol 26 (5) ◽  
Author(s):  
Miikka Kuutila ◽  
Mika Mäntylä ◽  
Maëlick Claes ◽  
Marko Elovainio ◽  
Bram Adams
Keyword(s):  
Individual Differences ◽  
Fixed Effects ◽  
Prediction Models ◽  
Well Being ◽  
Software Project ◽  
Structured Interviews ◽  
Software Developers ◽  
Software Repositories ◽  
Generalized Linear Mixed Effects ◽  
Individualized Prediction

AbstractReports of poor work well-being and fluctuating productivity in software engineering have been reported in both academic and popular sources. Understanding and predicting these issues through repository analysis might help manage software developers’ well-being. Our objective is to link data from software repositories, that is commit activity, communication, expressed sentiments, and job events, with measures of well-being obtained with a daily experience sampling questionnaire. To achieve our objective, we studied a single software project team for eight months in the software industry. Additionally, we performed semi-structured interviews to explain our results. The acquired quantitative data are analyzed with generalized linear mixed-effects models with autocorrelation structure. We find that individual variance accounts for most of the R2 values in models predicting developers’ experienced well-being and productivity. In other words, using software repository variables to predict developers’ well-being or productivity is challenging due to individual differences. Prediction models developed for each developer individually work better, with fixed effects R2 value of up to 0.24. The semi-structured interviews give insights into the well-being of software developers and the benefits of chat interaction. Our study suggests that individualized prediction models are needed for well-being and productivity prediction in software development.

scholarly journals GrimoireLab: A toolset for software development analytics

2021 ◽  
Vol 7 ◽  
pp. e601
Author(s):  
Santiago Dueñas ◽  
Valerio Cosentino ◽  
Jesus M. Gonzalez-Barahona ◽  
Alvaro del Castillo San Felix ◽  
Daniel Izquierdo-Cortazar ◽  
...  
Keyword(s):  
Software Development ◽  
Preliminary Analysis ◽  
Data Retrieval ◽  
Mining Software Repositories ◽  
Data Sources ◽  
Data Sets ◽  
Community Based ◽  
Academic Environments ◽  
Software Repositories ◽  
Main Components

Background After many years of research on software repositories, the knowledge for building mature, reusable tools that perform data retrieval, storage and basic analytics is readily available. However, there is still room to improvement in the area of reusable tools implementing this knowledge. Goal To produce a reusable toolset supporting the most common tasks when retrieving, curating and visualizing data from software repositories, allowing for the easy reproduction of data sets ready for more complex analytics, and sparing the researcher or the analyst of most of the tasks that can be automated. Method Use our experience in building tools in this domain to identify a collection of scenarios where a reusable toolset would be convenient, and the main components of such a toolset. Then build those components, and refine them incrementally using the feedback from their use in both commercial, community-based, and academic environments. Results GrimoireLab, an efficient toolset composed of five main components, supporting about 30 different kinds of data sources related to software development. It has been tested in many environments, for performing different kinds of studies, and providing different kinds of services. It features a common API for accessing the retrieved data, facilities for relating items from different data sources, semi-structured storage for easing later analysis and reproduction, and basic facilities for visualization, preliminary analysis and drill-down in the data. It is also modular, making it easy to support new kinds of data sources and analysis. Conclusions We present a mature toolset, widely tested in the field, that can help to improve the situation in the area of reusable tools for mining software repositories. We show some scenarios where it has already been used. We expect it will help to reduce the effort for doing studies or providing services in this area, leading to advances in reproducibility and comparison of results.

scholarly journals First analysis of software metrics

2015 ◽  
Author(s):  
Martin Fenner
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Software Metrics ◽  
Software Repositories ◽  
First Results

Last week I wrote about software.lagotto.io, an instance of the lagotto open source software collecting metrics for the about 1,400 software repositories included in Sciencetoolbox. In this post I want to report the first results analyzing the data.Number of software repositories (out of 1,404) ...

scholarly journals Analysing Software Repositories to Understand Software Evolution

2008 ◽  
pp. 37-67 ◽  
Author(s):  
Marco D’Ambros ◽  
Harald Gall ◽  
Michele Lanza ◽  
Martin Pinzger
Keyword(s):  
Software Evolution ◽  
Software Repositories
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