Ammonia: an approach for deriving project-specific bug patterns

Author:

Higo YoshikiORCID,Hayashi Shinpei,Hata Hideaki,Nagappan Meiyappan

Abstract

AbstractFinding and fixing buggy code is an important and cost-intensive maintenance task, and static analysis (SA) is one of the methods developers use to perform it. SA tools warn developers about potential bugs by scanning their source code for commonly occurring bug patterns, thus giving those developers opportunities to fix the warnings (potential bugs) before they release the software. Typically, SA tools scan for general bug patterns that are common to any software project (such as null pointer dereference), and not for project specific patterns. However, past research has pointed to this lack of customizability as a severe limiting issue in SA. Accordingly, in this paper, we propose an approach called , which is based on statically analyzing changes across the development history of a project, as a means to identify project-specific bug patterns. Furthermore, the bug patterns identified by our tool do not relate to just one developer or one specific commit, they reflect the project as a whole and compliment the warnings from other SA tools that identify general bug patterns. Herein, we report on the application of our implemented tool and approach to four Java projects: , , , and . The results obtained show that our tool could detect 19 project specific bug patterns across those four projects. Next, through manual analysis, we determined that six of those change patterns were actual bugs and submitted pull requests based on those bug patterns. As a result, five of the pull requests were merged.

Publisher

Springer Science and Business Media LLC

Subject

Software

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Tracking the Evolution of Static Code Warnings: The State-of-the-Art and a Better Approach;IEEE Transactions on Software Engineering;2024-03

2. Mining Python fix patterns via analyzing fine-grained source code changes;Empirical Software Engineering;2022-01-28

3. Tree-based Mining of Fine-grained Code Changes to Detect Unknown Change Patterns;2021 28th Asia-Pacific Software Engineering Conference (APSEC);2021-12

4. Sirius: Static Program Repair with Dependence Graph-Based Systematic Edit Patterns;2021 IEEE International Conference on Software Maintenance and Evolution (ICSME);2021-09

5. Inferring Bug Signatures to Detect Real Bugs;IEEE Transactions on Software Engineering;2020

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