Attention: not just another dataset for patch-correctness checking

Wang, Yuehan

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https://hdl.handle.net/2142/120093 Copy

Description

Title

Attention: not just another dataset for patch-correctness checking

Author(s)

Wang, Yuehan

Issue Date

2023-04-26

Director of Research (if dissertation) or Advisor (if thesis)

Zhang, Lingming

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Software Testing
• Automatic Program Repair
• Patch Correctness Checking

Abstract

Automated Program Repair (APR) techniques have drawn wide attention from both academia and industry. Meanwhile, one main limitation with the current state-of-the-art APR tools is that patches passing all the original tests are not necessarily the correct ones wanted by developers, i.e., the plausible patch problem. To date, various Patch-Correctness Checking (PCC) techniques have been proposed to address this important issue. However, they are only evaluated on very limited datasets as the APR tools used for generating such patches can only explore a small subset of the search space of possible patches, posing serious threats to external validity to existing PCC studies. In this paper, we construct an extensive PCC dataset (the largest manually labeled PCC dataset to our knowledge) to revisit all state-of-the-art PCC techniques. More specifically, our PCC dataset includes 1,988 patches generated from the recent PraPR APR tool, which leverages highly-optimized bytecode-level patch executions and can exhaustively explore all possible plausible patches within its large predefined search space (including well-known fixing patterns from various prior APR tools). Our extensive study of representative PCC techniques on the new dataset has revealed various surprising findings, including: 1. The assumption made by existing static PCC techniques that correct patches are more similar to buggy code than incorrect plausible patches no longer holds. 2. Some state-of-the-art learning-based techniques tend to suffer from the dataset over- fitting problem. 3. While dynamic techniques overall retain their effectiveness on our new dataset, their performance drops substantially on patches with more complicated changes. 4. The very recent naturalness-based techniques can substantially outperform traditional static techniques and could be a promising direction for PCC. Based on our findings, we also provide various guidelines and suggestions for advancing PCC in the near future.

Graduation Semester

2023-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Yuehan Wang

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