Evaluating Software Contribution Quality: Time-to-Modification Theory
October 15, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Vincil Bishop, Steven J Simske
arXiv ID
2410.11768
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
The durability and quality of software contributions are critical factors in the long-term maintainability of a codebase. This paper introduces the Time to Modification (TTM) Theory, a novel approach for quantifying code quality by measuring the time interval between a code segment's introduction and its first modification. TTM serves as a proxy for code durability, with longer intervals suggesting higher-quality, more stable contributions. This work builds on previous research, including the "Time-Delta Method for Measuring Software Development Contribution Rates" dissertation, from which it heavily borrows concepts and methodologies. By leveraging version control systems such as Git, TTM provides granular insights into the temporal stability of code at various levels ranging from individual lines to entire repositories. TTM Theory contributes to the software engineering field by offering a dynamic metric that captures the evolution of a codebase over time, complementing traditional metrics like code churn and cyclomatic complexity. This metric is particularly useful for predicting maintenance needs, optimizing developer performance assessments, and improving the sustainability of software systems. Integrating TTM into continuous integration pipelines enables real-time monitoring of code stability, helping teams identify areas of instability and reduce technical debt.
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