REVIEW 2 cited by
Program Repair: Automated vs. Manual
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Program Repair: Automated vs. Manual
read the original abstract
Various automated program repair (APR) techniques have been proposed to fix bugs automatically in the last decade. Although recent researches have made significant progress on the effectiveness and efficiency, it is still unclear how APR techniques perform with human intervention in a real debugging scenario. To bridge this gap, we conduct an extensive study to compare three state-of-the-art APR tools with manual program repair, and further investigate whether the assistance of APR tools (i.e., repair reports) can improve manual program repair. To that end, we recruit 20 participants for a controlled experiment, resulting in a total of 160 manual repair tasks and a questionnaire survey. The experiment reveals several notable observations that (1) manual program repair may be influenced by the frequency of repair actions sometimes; (2) APR tools are more efficient in terms of debugging time, while manual program repair tends to generate a correct patch with fewer attempts; (3) APR tools can further improve manual program repair regarding the number of correctly-fixed bugs, while there exists a negative impact on the patch correctness; (4) participants are used to consuming more time to identify incorrect patches, while they are still misguided easily; (5) participants are positive about the tools' repair performance, while they generally lack confidence about the usability in practice. Besides, we provide some guidelines for improving the usability of APR tools (e.g., the misleading information in reports and the observation of feedback).
Forward citations
Cited by 2 Pith papers
-
On the Effectiveness of Code Representation in Deep Learning-Based Automated Patch Correctness Assessment
Graph-based code representations such as Code Property Graphs achieve the highest accuracy (average 82.6%) in predicting patch correctness across 15 benchmarks and outperform sequence and tree representations when use...
-
GP-Tree: An in-memory spatial index combining adaptive grid cells with a prefix tree for efficient spatial querying
GP-Tree indexes spatial objects via adaptive grid-cell approximations in a prefix tree and claims up to 10× faster range, distance, and k-NN queries than STR-Tree and Quad-Tree.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.