GrimoireLab: A toolset for software development analytics

Abstract

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.

Integrated Visual Software Analytics on the GitHub Platform

“…The former use case is supported by platforms such as Sonarqube and the source{d} Community Edition. The latter use case is supported by research platforms such as MetricMiner [52] and GrimoireLab [53]. For metrics already measured by GitHub, there is also Google BigQuery for Github (https://cloud.google.com/blog/topics/publicdatasets/github-on-bigquery-analyze-all-the-open-source-code, accessed on 16 January 2024),which allows for access to data using an SQL interface.…”

PANDORA: Continuous Mining Software Repository and Dataset Generation

“…The process is based on the ETL plugin that can be either API based, or executed on the locally cloned repositories. • Data Analysis: enables to integrate data-analysis plugins that will be executed in Apache Spark 13 , each using a specific methodology (Machine Learning/Statistical Analysis) to solve a specific task. • Dashboard: visualization tool based on Apache Superset 14 , used for inspecting and visualizing the data and the results of the analysis performed in the Data Analysis block.…”

“…Data Analysis. The data analysis process can be executed as python script in Apache Spark 13 . Spark enables to execute analysis on a distributed cluster and it is used to prepare data, train, test and validate a range of Machine Learning and statistical models.…”

“…Finally, GrimoireLab, by Dueñas et al [13], is a toolset integrating modules to retrieve process-related information from multiple types of software repositories. It also provides a dashboard for the interactive visualization of the data retrieved.…”

Maintaining Repositories, Databases, and Digital Collections in Memory Institutions: An Integrative Review

“…Five items (6%) were published in other venues, including magazines and newsletters. We note that our initial query did retrieve numerous computer science research papers related to database migration, but after abstract review we determined that many of these were not relevant to this study, as they focused on topics like "software analytics toolset development" (Dueñas et al, 2021), or the description of domain-specific databases without any discussion of sustainability or curation (Tabakmakher et al, 2019). Similarly, most papers described database curation work taking place within an academic library (n=16) or institutional repository (n=23).…”