3,547 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
Dissection of a Bug Dataset: Anatomy of 395 Patches from Defects4J
Well-designed and publicly available datasets of bugs are an invaluable asset
to advance research fields such as fault localization and program repair as
they allow directly and fairly comparison between competing techniques and also
the replication of experiments. These datasets need to be deeply understood by
researchers: the answer for questions like "which bugs can my technique
handle?" and "for which bugs is my technique effective?" depends on the
comprehension of properties related to bugs and their patches. However, such
properties are usually not included in the datasets, and there is still no
widely adopted methodology for characterizing bugs and patches. In this work,
we deeply study 395 patches of the Defects4J dataset. Quantitative properties
(patch size and spreading) were automatically extracted, whereas qualitative
ones (repair actions and patterns) were manually extracted using a thematic
analysis-based approach. We found that 1) the median size of Defects4J patches
is four lines, and almost 30% of the patches contain only addition of lines; 2)
92% of the patches change only one file, and 38% has no spreading at all; 3)
the top-3 most applied repair actions are addition of method calls,
conditionals, and assignments, occurring in 77% of the patches; and 4) nine
repair patterns were found for 95% of the patches, where the most prevalent,
appearing in 43% of the patches, is on conditional blocks. These results are
useful for researchers to perform advanced analysis on their techniques'
results based on Defects4J. Moreover, our set of properties can be used to
characterize and compare different bug datasets.Comment: Accepted for SANER'18 (25th edition of IEEE International Conference
on Software Analysis, Evolution and Reengineering), Campobasso, Ital
Automated Fixing of Programs with Contracts
This paper describes AutoFix, an automatic debugging technique that can fix
faults in general-purpose software. To provide high-quality fix suggestions and
to enable automation of the whole debugging process, AutoFix relies on the
presence of simple specification elements in the form of contracts (such as
pre- and postconditions). Using contracts enhances the precision of dynamic
analysis techniques for fault detection and localization, and for validating
fixes. The only required user input to the AutoFix supporting tool is then a
faulty program annotated with contracts; the tool produces a collection of
validated fixes for the fault ranked according to an estimate of their
suitability.
In an extensive experimental evaluation, we applied AutoFix to over 200
faults in four code bases of different maturity and quality (of implementation
and of contracts). AutoFix successfully fixed 42% of the faults, producing, in
the majority of cases, corrections of quality comparable to those competent
programmers would write; the used computational resources were modest, with an
average time per fix below 20 minutes on commodity hardware. These figures
compare favorably to the state of the art in automated program fixing, and
demonstrate that the AutoFix approach is successfully applicable to reduce the
debugging burden in real-world scenarios.Comment: Minor changes after proofreadin
Amortising the Cost of Mutation Based Fault Localisation using Statistical Inference
Mutation analysis can effectively capture the dependency between source code
and test results. This has been exploited by Mutation Based Fault Localisation
(MBFL) techniques. However, MBFL techniques suffer from the need to expend the
high cost of mutation analysis after the observation of failures, which may
present a challenge for its practical adoption. We introduce SIMFL (Statistical
Inference for Mutation-based Fault Localisation), an MBFL technique that allows
users to perform the mutation analysis in advance against an earlier version of
the system. SIMFL uses mutants as artificial faults and aims to learn the
failure patterns among test cases against different locations of mutations.
Once a failure is observed, SIMFL requires either almost no or very small
additional cost for analysis, depending on the used inference model. An
empirical evaluation of SIMFL using 355 faults in Defects4J shows that SIMFL
can successfully localise up to 103 faults at the top, and 152 faults within
the top five, on par with state-of-the-art alternatives. The cost of mutation
analysis can be further reduced by mutation sampling: SIMFL retains over 80% of
its localisation accuracy at the top rank when using only 10% of generated
mutants, compared to results obtained without sampling
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