Compiling static software metrics for reliability and maintainability from GitHub repositories

Ludwig, Jeremy; Xu, Susu; Webber, Frederick

doi:10.1109/smc.2017.8122569

Cited by 10 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Example applications for analyzing git repositories include capturing static and dynamic software metrics [18][19][20], locating expertise among developers [21], or measuring environmental sustainability [22]. The extraction of relevant data requires efficient processing tools, e.g., for compiling software metrics [23]. An example of such a tool is PyDriller, which allows the efficient extraction of software metrics from a git repository [24].…”

Section: Tools For Mining Software Repositoriesmentioning

confidence: 99%

Integrated Visual Software Analytics on the GitHub Platform

Scheibel,

Blum,

Lauterbach

et al. 2024

Computers

View full text Add to dashboard Cite

Readily available software analysis and analytics tools are often operated within external services, where the measured software analysis data are kept internally and no external access to the data is available. We propose an approach to integrate visual software analysis on the GitHub platform by leveraging GitHub Actions and the GitHub API, covering both analysis and visualization. The process is to perform software analysis for each commit, e.g., static source code complexity metrics, and augment the commit using the resulting data, stored as git objects within the same repository. We show that this approach is feasible by integrating it into 64 open source TypeScript projects. Furthermore, we analyze the impact on Continuous Integration (CI) run time and repository storage. The stored software analysis data are externally accessible to allow for visualization tools, such as software maps. The effort to integrate our approach is limited to enabling the analysis component within a project’s CI on GitHub and embed an HTML snippet into the project’s website for visualization. This enables a large amount of projects to have access to software analysis as well as provide means to communicate the current status of a project.

show abstract

Section: Tools For Mining Software Repositoriesmentioning

confidence: 99%

Integrated Visual Software Analytics on the GitHub Platform

Scheibel,

Blum,

Lauterbach

et al. 2024

Computers

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, there is not much research made into architecture rules visualization. Only a few works have delved into this topic 14‐18 …”

Section: Related Workmentioning

confidence: 99%

“…Ludwig et al 16 generate automated HTML reports based on the results of the aggregation of architectural metrics and complexity metrics. A visualization of the components of the project is developed where components and files are represented as boxes; the boxes' size represents the number LOC of the file, and the boxes' shade of color represents the complexity of the file.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Architecture violations detection and visualization in the continuous integration pipeline

et al. 2021

View full text Add to dashboard Cite

New code in projects can introduce violations that deviate the code implementation from the intended architecture. This process is known as architecture erosion. In this article, we propose an approach for recovering the implemented architecture, and detecting violations when comparing it with the intended architecture. Given a code repository, the continuous integration pipeline calls the solution to detect the incidences of architecture violations as well as some quality and social metrics. This data is presented in metric‐centered views that help development teams to manage architecture erosion. Our approach is based on model‐driven engineering techniques since models serve to represent the code, and a model‐based pattern language helps us to automate the search for violation occurrences and execute corresponding actions (e.g., creation/assignment of issues). We confirm the approach benefits in a real project implemented by a software developing company, in a sample project available on the internet, and in a software development course, including 20 projects, where every single project decreases its architecture violations density through time.

show abstract

“…is metric is hence not defined on a single version of the software project, but it is tailored to analyze the evolution of the source code. e assumption between the usage of Chidamber and Kemerer DIT, depth of inheritance tree [5,14,26,43,44], [20,21,27,32], [23,28,29,39], [35,47] 15 13 Chidamber and Kemerer LCOM, lack of cohesion in methods [5,14,26,43,44], [20,27,28,32], [23,36,39,46,47] 14 12 Chidamber and Kemerer NOC, number of children [5,14,26,43,44], [20,27,32,39], [23,29,35,47] 13 11 Chidamber and Kemerer RFC, response for class [5,15,19,26,…”

Section: (Iii) Change (Number Of Lines Changed In the Class)mentioning

confidence: 99%

A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Ardito

Coppola

Barbato³

et al. 2020

Scientific Programming

View full text Add to dashboard Cite

Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.

show abstract

Compiling static software metrics for reliability and maintainability from GitHub repositories

Cited by 10 publications

References 21 publications

Integrated Visual Software Analytics on the GitHub Platform

Integrated Visual Software Analytics on the GitHub Platform

Architecture violations detection and visualization in the continuous integration pipeline

A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Contact Info

Product

Resources

About