An Empirical Analysis of Git Commit Logs for Potential Inconsistency in Code Clones
September 13, 2024 Β· Declared Dead Β· π IEEE Working Conference on Source Code Analysis and Manipulation
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Reishi Yokomori, Katsuro Inoue
arXiv ID
2409.08555
Category
cs.SE: Software Engineering
Citations
0
Venue
IEEE Working Conference on Source Code Analysis and Manipulation
Last Checked
5 months ago
Abstract
Code clones are code snippets that are identical or similar to other snippets within the same or different files. They are often created through copy-and-paste practices and modified during development and maintenance activities. Since a pair of code clones, known as a clone pair, has a possible logical coupling between them, it is expected that changes to each snippet are made simultaneously (co-changed) and consistently. There is extensive research on code clones, including studies related to the co-change of clones; however, detailed analysis of commit logs for code clone pairs has been limited. In this paper, we investigate the commit logs of code snippets from clone pairs, using the git-log command to extract changes to cloned code snippets. We analyzed 45 repositories owned by the Apache Software Foundation on GitHub and addressed three research questions regarding commit frequency, co-change ratio, and commit patterns. Our findings indicate that (1) on average, clone snippets are changed infrequently, typically only two or three times throughout their lifetime, (2) the ratio of co-changes is about half of all clone changes, with 10-20\% of co-changed commits being concerning (potentially inconsistent), and (3) 35-65\% of all clone pairs being classified as concerning clone pairs (potentially inconsistent clone pairs). These results suggest the need for a consistent management system through the commit timeline of clones.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted