23,446 research outputs found
You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems
Properly benchmarking Automated Program Repair (APR) systems should
contribute to the development and adoption of the research outputs by
practitioners. To that end, the research community must ensure that it reaches
significant milestones by reliably comparing state-of-the-art tools for a
better understanding of their strengths and weaknesses. In this work, we
identify and investigate a practical bias caused by the fault localization (FL)
step in a repair pipeline. We propose to highlight the different fault
localization configurations used in the literature, and their impact on APR
systems when applied to the Defects4J benchmark. Then, we explore the
performance variations that can be achieved by `tweaking' the FL step.
Eventually, we expect to create a new momentum for (1) full disclosure of APR
experimental procedures with respect to FL, (2) realistic expectations of
repairing bugs in Defects4J, as well as (3) reliable performance comparison
among the state-of-the-art APR systems, and against the baseline performance
results of our thoroughly assessed kPAR repair tool. Our main findings include:
(a) only a subset of Defects4J bugs can be currently localized by commonly-used
FL techniques; (b) current practice of comparing state-of-the-art APR systems
(i.e., counting the number of fixed bugs) is potentially misleading due to the
bias of FL configurations; and (c) APR authors do not properly qualify their
performance achievement with respect to the different tuning parameters
implemented in APR systems.Comment: Accepted by ICST 201
Identifying Patch Correctness in Test-Based Program Repair
Test-based automatic program repair has attracted a lot of attention in
recent years. However, the test suites in practice are often too weak to
guarantee correctness and existing approaches often generate a large number of
incorrect patches.
To reduce the number of incorrect patches generated, we propose a novel
approach that heuristically determines the correctness of the generated
patches. The core idea is to exploit the behavior similarity of test case
executions. The passing tests on original and patched programs are likely to
behave similarly while the failing tests on original and patched programs are
likely to behave differently. Also, if two tests exhibit similar runtime
behavior, the two tests are likely to have the same test results. Based on
these observations, we generate new test inputs to enhance the test suites and
use their behavior similarity to determine patch correctness.
Our approach is evaluated on a dataset consisting of 139 patches generated
from existing program repair systems including jGenProg, Nopol, jKali, ACS and
HDRepair. Our approach successfully prevented 56.3\% of the incorrect patches
to be generated, without blocking any correct patches.Comment: ICSE 201
- …