5,999 research outputs found
A Critical Review of "Automatic Patch Generation Learned from Human-Written Patches": Essay on the Problem Statement and the Evaluation of Automatic Software Repair
At ICSE'2013, there was the first session ever dedicated to automatic program
repair. In this session, Kim et al. presented PAR, a novel template-based
approach for fixing Java bugs. We strongly disagree with key points of this
paper. Our critical review has two goals. First, we aim at explaining why we
disagree with Kim and colleagues and why the reasons behind this disagreement
are important for research on automatic software repair in general. Second, we
aim at contributing to the field with a clarification of the essential ideas
behind automatic software repair. In particular we discuss the main evaluation
criteria of automatic software repair: understandability, correctness and
completeness. We show that depending on how one sets up the repair scenario,
the evaluation goals may be contradictory. Eventually, we discuss the nature of
fix acceptability and its relation to the notion of software correctness.Comment: ICSE 2014, India (2014
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
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
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
An Empirical analysis of Open Source Software Defects data through Software Reliability Growth Models
The purpose of this study is to analyze the reliability growth of Open Source Software (OSS) using Software Reliability Growth Models (SRGM). This study uses defects data of twenty five different releases of five OSS projects. For each release of the selected projects two types of datasets have been created; datasets developed with respect to defect creation date (created date DS) and datasets developed with respect to defect updated date (updated date DS). These defects datasets are modelled by eight SRGMs; Musa Okumoto, Inflection S-Shaped, Goel Okumoto, Delayed S-Shaped, Logistic, Gompertz, Yamada Exponential, and Generalized Goel Model. These models are chosen due to their widespread use in the literature. The SRGMs are fitted to both types of defects datasets of each project and the their fitting and prediction capabilities are analysed in order to study the OSS reliability growth with respect to defects creation and defects updating time because defect analysis can be used as a constructive reliability predictor. Results show that SRGMs fitting capabilities and prediction qualities directly increase when defects creation date is used for developing OSS defect datasets to characterize the reliability growth of OSS. Hence OSS reliability growth can be characterized with SRGM in a better way if the defect creation date is taken instead of defects updating (fixing) date while developing OSS defects datasets in their reliability modellin
IntRepair: Informed Repairing of Integer Overflows
Integer overflows have threatened software applications for decades. Thus, in
this paper, we propose a novel technique to provide automatic repairs of
integer overflows in C source code. Our technique, based on static symbolic
execution, fuses detection, repair generation and validation. This technique is
implemented in a prototype named IntRepair. We applied IntRepair to 2,052C
programs (approx. 1 million lines of code) contained in SAMATE's Juliet test
suite and 50 synthesized programs that range up to 20KLOC. Our experimental
results show that IntRepair is able to effectively detect integer overflows and
successfully repair them, while only increasing the source code (LOC) and
binary (Kb) size by around 1%, respectively. Further, we present the results of
a user study with 30 participants which shows that IntRepair repairs are more
than 10x efficient as compared to manually generated code repairsComment: Accepted for publication at the IEEE TSE journal. arXiv admin note:
text overlap with arXiv:1710.0372
Synchronous wearable wireless body sensor network composed of autonomous textile nodes
A novel, fully-autonomous, wearable, wireless sensor network is presented, where each flexible textile node performs cooperative synchronous acquisition and distributed event detection. Computationally efficient situational-awareness algorithms are implemented on the low-power microcontroller present on each flexible node. The detected events are wirelessly transmitted to a base station, directly, as well as forwarded by other on-body nodes. For each node, a dual-polarized textile patch antenna serves as a platform for the flexible electronic circuitry. Therefore, the system is particularly suitable for comfortable and unobtrusive integration into garments. In the meantime, polarization diversity can be exploited to improve the reliability and energy-efficiency of the wireless transmission. Extensive experiments in realistic conditions have demonstrated that this new autonomous, body-centric, textile-antenna, wireless sensor network is able to correctly detect different operating conditions of a firefighter during an intervention. By relying on four network nodes integrated into the protective garment, this functionality is implemented locally, on the body, and in real time. In addition, the received sensor data are reliably transferred to a central access point at the command post, for more detailed and more comprehensive real-time visualization. This information provides coordinators and commanders with situational awareness of the entire rescue operation. A statistical analysis of measured on-body node-to-node, as well as off-body person-to-person channels is included, confirming the reliability of the communication system
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