1,397 research outputs found
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Patching is a common activity in software development. It is generally
performed on a source code base to address bugs or add new functionalities. In
this context, given the recurrence of bugs across projects, the associated
similar patches can be leveraged to extract generic fix actions. While the
literature includes various approaches leveraging similarity among patches to
guide program repair, these approaches often do not yield fix patterns that are
tractable and reusable as actionable input to APR systems. In this paper, we
propose a systematic and automated approach to mining relevant and actionable
fix patterns based on an iterative clustering strategy applied to atomic
changes within patches. The goal of FixMiner is thus to infer separate and
reusable fix patterns that can be leveraged in other patch generation systems.
Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree
structure of the edit scripts that captures the AST-level context of the code
changes. FixMiner uses different tree representations of Rich Edit Scripts for
each round of clustering to identify similar changes. These are abstract syntax
trees, edit actions trees, and code context trees. We have evaluated FixMiner
on thousands of software patches collected from open source projects.
Preliminary results show that we are able to mine accurate patterns,
efficiently exploiting change information in Rich Edit Scripts. We further
integrated the mined patterns to an automated program repair prototype,
PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J
benchmark. Beyond this quantitative performance, we show that the mined fix
patterns are sufficiently relevant to produce patches with a high probability
of correctness: 81% of PARFixMiner's generated plausible patches are correct.Comment: 31 pages, 11 figure
Locating bugs without looking back
Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We compare our approach to eight others, using their own five metrics on their own six open source projects. Out of 30 performance indicators, we improve 27 and equal 2. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. These results show the applicability of our approach to software projects without history
Source Code Retrieval from Large Software Libraries for Automatic Bug Localization
This dissertation advances the state-of-the-art in information retrieval (IR) based approaches to automatic bug localization in software. In an IR-based approach, one first creates a search engine using a probabilistic or a deterministic model for the files in a software library. Subsequently, a bug report is treated as a query to the search engine for retrieving the files relevant to the bug. With regard to the new work presented, we first demonstrate the importance of taking version histories of the files into account for achieving significant improvements in the precision with which the files related to a bug are located. This is motivated by the realization that the files that have not changed in a long time are likely to have ``stabilized and are therefore less likely to contain bugs. Subsequently, we look at the difficulties created by the fact that developers frequently use abbreviations and concatenations that are not likely to be familiar to someone trying to locate the files related to a bug. We show how an initial query can be automatically reformulated to include the relevant actual terms in the files by an analysis of the files retrieved in response to the original query for terms that are proximal to the original query terms. The last part of this dissertation generalizes our term-proximity based work by using Markov Random Fields (MRF) to model the inter-term dependencies in a query vis-a-vis the files. Our MRF work redresses one of the major defects of the most commonly used modeling approaches in IR, which is the loss of all inter-term relationships in the documents
Mining Fix Patterns for FindBugs Violations
In this paper, we first collect and track a large number of fixed and unfixed
violations across revisions of software.
The empirical analyses reveal that there are discrepancies in the
distributions of violations that are detected and those that are fixed, in
terms of occurrences, spread and categories, which can provide insights into
prioritizing violations.
To automatically identify patterns in violations and their fixes, we propose
an approach that utilizes convolutional neural networks to learn features and
clustering to regroup similar instances. We then evaluate the usefulness of the
identified fix patterns by applying them to unfixed violations.
The results show that developers will accept and merge a majority (69/116) of
fixes generated from the inferred fix patterns. It is also noteworthy that the
yielded patterns are applicable to four real bugs in the Defects4J major
benchmark for software testing and automated repair.Comment: Accepted for IEEE Transactions on Software Engineerin
Fonte: Finding Bug Inducing Commits from Failures
A Bug Inducing Commit (BIC) is a commit that introduces a software bug into
the codebase. Knowing the relevant BIC for a given bug can provide valuable
information for debugging as well as bug triaging. However, existing BIC
identification techniques are either too expensive (because they require the
failing tests to be executed against previous versions for bisection) or
inapplicable at the debugging time (because they require post hoc artefacts
such as bug reports or bug fixes). We propose Fonte, an efficient and accurate
BIC identification technique that only requires test coverage. Fonte combines
Fault Localisation (FL) with BIC identification and ranks commits based on the
suspiciousness of the code elements that they modified. Fonte reduces the
search space of BICs using failure coverage as well as a filter that detects
commits that are merely style changes. Our empirical evaluation using 130
real-world BICs shows that Fonte significantly outperforms state-of-the-art BIC
identification techniques based on Information Retrieval as well as neural code
embedding models, achieving at least 39% higher MRR. We also report that the
ranking scores produced by Fonte can be used to perform weighted bisection,
further reducing the cost of BIC identification. Finally, we apply Fonte to a
large-scale industry project with over 10M lines of code, and show that it can
rank the actual BIC within the top five commits for 87% of the studied real
batch-testing failures, and save the BIC inspection cost by 32% on average.Comment: accepted to ICSE'23 (not the final version
Efficient Information Retrieval for Software Bug Localization
Software systems are often shipped with defects. When a bug is reported, developers use the information available in the associated report to locate source code fragments that need to be modified to fix the bug. However, as software systems evolve in size and complexity, bug localization can become a tedious and time-consuming process. Contemporary bug localization tools utilize Information Retrieval (IR) methods for automated support to minimize the manual effort. IR methods exploit the textual content of bug reports to capture and rank relevant buggy source files. However, for an IR-based bug localization tool to be useful, it must achieve adequate retrieval accuracy. Lower precision and recall can leave developers with large amounts of incorrect information to wade through. Motivated by these observations, in this dissertation, we propose a new paradigm of information-theoretic IR methods to support bug localization tasks in software systems. These methods exploit the co-occurrence patterns of code terms in
software systems to reveal latent semantic information that other methods often fail to capture. We further investigate the impact of combining various IR methods on the retrieval accuracy of bug localization engines. The main assumption is that different IR methods, targeting different dimensions of similarity between software artifacts, can enhance the confidence in each other\u27s results. Furthermore, we propose a novel approach for enhancing the performance of IR-enabled bug localization methods in the context of Open-Source Software (OSS). The proposed approach exploits knowledge from previously resolved bugs to help localize new bugs. Our analysis uses multiple datasets generated for multiple open-source and closed source projects. Our results show that a) information-theoretic IR methods can significantly outperform classical IR methods in bug localization tasks, b) optimized IR-hybrids can significantly outperform individual IR methods, and near-optimal global configurations can be determined for different combinations of IR methods, and c) information extracted from previously resolved bug reports can significantly enhance the accuracy of IR-enabled bug localization methods in OSS
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