Preliminary Study on the Reproducibility of Fix Templates in Static Analysis Tool

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automated Program Repair (APR) automatically generates patches for identified defects. As a result, APR can encourage novice students to learn coding by providing appropriate patches. Since students have their coding conventions, we should be able to deal with many types of defects. Existing studies have used predefined RuleId-driven templates to automatically fix defects in Static Analysis Tools(SATs). However, the community periodically adds, deletes, or changes SAT's RuleIds. This is difficult for existing RuleId-based templates to reflect those changes immediately. Existing studies only cover about 10 RuleIds, making it difficult to address all defects faced by all students. Therefore, it is necessary to establish appropriate criteria for classifying templates. The SAT has a predefined format for how Error Messages are written, and since Error Messages contain fixing actions, defects with similar Error Messages tend to have similar fixing actions. These characteristics of the Error Message are suitable for reproducible template classification criteria. Our preliminary study demonstrated that by classifying patterns based on Error Messages, we could effectively address various defects, including those in different programming languages, using a single template. This means that if a newly added RuleId corresponds to an Error Message format already in the predefined Error Message-based template, it can be modified without additional effort. We plan to construct reproducible templates for each Error Message and provide ongoing patching of defects to students.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 35th International Conference on Software Engineering Education and Training, CSEE and T 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages189-190
Number of pages2
ISBN (Electronic)9798350322026
DOIs
StatePublished - 2023
Event35th IEEE International Conference on Software Engineering Education and Training, CSEE and T 2023 - Tokyo, Japan
Duration: 7 Aug 20239 Aug 2023

Publication series

NameSoftware Engineering Education Conference, Proceedings
Volume2023-August
ISSN (Print)1093-0175

Conference

Conference35th IEEE International Conference on Software Engineering Education and Training, CSEE and T 2023
Country/TerritoryJapan
CityTokyo
Period7/08/239/08/23

Keywords

  • Automated Program Repair
  • Fix Pattern
  • Novice Programs
  • Static Analysis Tool

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