Sirius: Static Program Repair with Dependence Graph-Based Systematic Edit Patterns

Noda, Kohei; Yokoyama, Haruki; Kikuchi, Shinji

doi:10.1109/icsme52107.2021.00045

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…Researchers utilize fg-PDGs for many applications. For example, Nguyen et al [34], Smirnov et al [46], and Dilhara et al [21], used fgPDG on Java and Python systems to mine semantically equivalent repeated code changes, while Noda et al [47] used fgPDG to mine repeated bug fixes and repair unfixed similar bugs.…”

Section: ) Transformation Rules (Tr): Modern "Transformation By Examp...mentioning

confidence: 99%

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PYEVOLVE: Automating Frequent Code Changes in Python ML Systems

Dilhara

Dig

Ketkar

2023

2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

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Because of the naturalness of software and the rapid evolution of Machine Learning (ML) techniques, frequently repeated code change patterns (CPATs) occur often. They range from simple API migrations to changes involving several complex control structures such as for loops. While manually performing CPATs is tedious, the current state-of-the-art techniques for inferring transformation rules are not advanced enough to handle unseen variants of complex CPATs, resulting in a low recall rate. In this paper we present a novel, automated workflow that mines CPATs, infers the transformation rules, and then transplants them automatically to new target sites. We designed, implemented, evaluated and released this in a tool, PYEVOLVE. At its core is a novel data-flow, control-flow aware transformation rule inference engine. Our technique allows us to advance the state-of-the-art for transformation-by-example tools; without it, 70% of the code changes that PYEVOLVE transforms would not be possible to automate. Our thorough empirical evaluation of over 40,000 transformations shows 97% precision and 94% recall. By accepting 90% of CPATs generated by PYEVOLVE in famous open-source projects, developers confirmed its changes are useful.

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Section: ) Transformation Rules (Tr): Modern "Transformation By Examp...mentioning

confidence: 99%

“…For the automated validation, we were inspired by the steps that Noda et al [47] used to evaluate automated bug patches. We denote the AST nodes that represent the T A LHS and T A RHS as A LHS and A RHS , respectively.…”

Section: A Rq1: What Is the Effectiveness Of Pyevolve In Generating C...mentioning

confidence: 99%

PYEVOLVE: Automating Frequent Code Changes in Python ML Systems

Dilhara

Dig

Ketkar

2023

2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)

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Automatic Repair Method for Null Pointer Dereferences Guided by Program Dependency Graph

2022

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Automatic program repair (APR) is an effective technique for eliminating defects. The repair of null pointer dereferences, as the most common defects, requires accurate dependencies among statements to determine where to repair and how to repair. In order to precisely identify the data and control dependencies, the program dependency graph is adopted. Based on the symmetry among a large number of patches, we propose four repair mechanisms in this passage, namely the assignment mechanism, restraint mechanism, evading mechanism, and transfer mechanism, and employ the decision tree algorithm to match the best repair mechanism for defects. The four repair mechanisms locate the accurate repair position using the program dependency graph, and generate candidate patches by reassigning the null pointer with an appropriate value, making a judgment for the null value in advance, or throwing an exception. Our method was implemented in the repair tool DTSFix, which supports the automatic repair of null pointer dereference in Java programs. The experimental result on Defects4J shows that 73% of null pointer dereferences are successfully repaired by DTSFix, and that the generated candidate patches do not contain over-fitting patches.

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Sirius: Static Program Repair with Dependence Graph-Based Systematic Edit Patterns

Cited by 2 publications

References 44 publications

PYEVOLVE: Automating Frequent Code Changes in Python ML Systems

PYEVOLVE: Automating Frequent Code Changes in Python ML Systems

Automatic Repair Method for Null Pointer Dereferences Guided by Program Dependency Graph

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