Automatically generated patches as debugging aids: a human study

Tao, Yida; Kim, Jindae; Kim, Sunghun; Xu, Chang

doi:10.1145/2635868.2635873

Cited by 65 publications

(58 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work indicates that machine-generated patches are slightly less maintainable than humanwritten ones; hence, patches by automatic repair could be used as the patches written by humans. Another case study is conducted by Tao et al [48]. They investigate the possibility of leveraging patches by automatic repair to assist the process of debugging by humans.…”

Section: Empirical Foundations Of Repairmentioning

confidence: 99%

Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

Xuan

Martínez

Demarco

et al. 2017

IIEEE Trans. Software Eng.

330

268

View full text Add to dashboard Cite

International audienceWe propose NOPOL, an approach to automatic repair of buggy conditional statements (i.e., if-then-else statements). This approach takes a buggy program as well as a test suite as input and generates a patch with a conditional expression as output. The test suite is required to contain passing test cases to model the expected behavior of the program and at least one failing test case that reveals the bug to be repaired. The process of NOPOL consists of three major phases. First, NOPOL employs angelic fix localization to identify expected values of a condition during the test execution. Second, runtime trace collection is used to collect variables and their actual values, including primitive data types and objected-oriented features (e.g., nullness checks), to serve as building blocks for patch generation. Third, NOPOL encodes these collected data into an instance of a Satisfiability Modulo Theory (SMT) problem; then a feasible solution to the SMT instance is translated back into a code patch. We evaluate NOPOL on 22 real-world bugs (16 bugs with buggy IF conditions and 6 bugs with missing preconditions) on two large open-source projects, namely Apache Commons Math and Apache Commons Lang. Empirical analysis on these bugs shows that our approach can effectively fix bugs with buggy IF conditions and missing preconditions. We illustrate the capabilities and limitations of NOPOL using case studies of real bug fixes

show abstract

Section: Empirical Foundations Of Repairmentioning

confidence: 99%

Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

Xuan

Martínez

Demarco

et al. 2017

IIEEE Trans. Software Eng.

330

268

View full text Add to dashboard Cite

show abstract

“…However, a smaller subset of these studies have included meaningful evaluation of developer interaction with such patches through user studies. Previous work, has explored patch understandability and maintainability using questions [5], comparing automatically generated patches between two systems [7], or based on providing users with no patches or a single (possibly) correct patch [27]. Our own work suggests that user studies can reveal new directions for automatic patch generation research to improve useability.…”

Section: Related Workmentioning

confidence: 99%

Characterizing Developer Use of Automatically Generated Patches

Cambronero

Shen

Cito

et al. 2019

2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

View full text Add to dashboard Cite

We present a study that characterizes the way developers use automatically generated patches when fixing software defects. Our study tasked two groups of developers with repairing defects in C programs. Both groups were provided with the defective line of code. One was also provided with five automatically generated and validated patches, all of which modified the defective line of code, and one of which was correct. Contrary to our initial expectations, the group with access to the generated patches did not produce more correct patches and did not produce patches in less time. We characterize the main behaviors observed in experimental subjects: a focus on understanding the defect and the relationship of the patches to the original source code. Based on this characterization, we highlight various potentially productive directions for future developer-centric automatic patch generation systems.

show abstract

“…MintHint generates repair hints based on statistical analysis. Tao et al investigated how automatically generated patches can be used as debugging aids. Bissyandé suggests patches for bug reports based on the history of patches .…”

Section: Related Workmentioning

confidence: 99%

Watch out for this commit! A study of influential software changes

Kim

et al. 2019

J Software Evolu Process

View full text Add to dashboard Cite

One single code change can significantly influence a wide range of software systems and their users. For example, (a) adding a new feature can spread defects in several modules, while (b) changing an API method can improve the performance of all client programs. Unfortunately, developers often may not clearly know whether code changes are influential at commit time. This paper investigates influential software changes and proposes an approach to identify them immediately when they are applied. Our goals are to (a) identify existing influential changes (ICs) in software projects, (b) understand their characteristics, and (c) build a classification model of ICs to help developers find and address them early. We first conduct a post‐mortem analysis to discover existing influential changes by using intuitions (eg, changes referred by other changes). Then, we re‐categorize all identified changes through an open‐card sorting process. Subsequently, we conduct a survey with about 100 developers to finalize a taxonomy. Finally, from our ground truth, we extract features, including metrics such as the complexity of changes and file centrality in co‐change graphs to build machine learning classifiers. The experiment results show that our classification model with random samples achieves 86.8% precision, 74% recall, and 80.4% F‐measure, respectively.

show abstract

Automatically generated patches as debugging aids: a human study

Cited by 65 publications

References 31 publications

Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

Characterizing Developer Use of Automatically Generated Patches

Watch out for this commit! A study of influential software changes

Contact Info

Product

Resources

About