Debt-Prone Bugs: Technical Debt in Software Maintenance
April 16, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Jifeng Xuan, Yan Hu, He Jiang
arXiv ID
1704.04766
Category
cs.SE: Software Engineering
Citations
17
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Fixing bugs is an important phase in software development and maintenance. In practice, the process of bug fixing may conflict with the release schedule. Such confliction leads to a trade-off between software quality and release schedule, which is known as the technical debt metaphor. In this article, we propose the concept of debt-prone bugs to model the technical debt in software maintenance. We identify three types of debt-prone bugs, namely tag bugs, reopened bugs, and duplicate bugs. A case study on Mozilla is conducted to examine the impact of debt-prone bugs in software products. We investigate the correlation between debt-prone bugs and the product quality. For a product under development, we build prediction models based on historical products to predict the time cost of fixing bugs. The result shows that identifying debt-prone bugs can assist in monitoring and improving software quality.
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