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In this paper, we do automatic correctness assessment for patches generated by program repair systems. We consider the human-written patch as ground truth oracle and randomly generate tests based on it, a technique proposed by Shamshiri et al., called Random testing with Ground Truth (RGT) in this paper. We build a curated dataset of 638 patches for Defects4J generated by 14 state-of-the-art repair systems, we evaluate automated patch assessment on this dataset. The results of this study are novel and significant: First, we improve the state of the art performance of automatic patch assessment with RGT by 190% by improving the oracle; Second, we show that RGT is reliable enough to help scientists to do overfitting analysis when they evaluate program repair systems; Third, we improve the external validity of the program repair knowledge with the largest study ever.

This paper is about understanding the nature of bug fixing by analyzing thousands of bug fix transactions of software repositories. It then places this learned knowledge in the context of automated program repair. We give extensive empirical results on the nature of human bug fixes at a large scale and a fine granularity with abstract syntax tree differencing. We set up mathematical reasoning on the search space of automated repair and the time to navigate through it. By applying our method on 14 repositories of Java software and 89,993 versioning transactions, we show that not all probabilistic repair models are equivalent.

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.

Build automation tools and package managers have a profound influence on software development. They facilitate the reuse of third-party libraries, support a clear separation between the application’s code and its external dependencies, and automate several software development tasks. However, the wide adoption of these tools introduces new challenges related to dependency management. In this paper, we propose an original study of one such challenge: the emergence of bloated dependencies. Bloated dependencies are libraries that are packaged with the application’s compiled code but that are actually not necessary to build and run the application. They artificially grow the size of the built binary and increase maintenance effort. We propose DepClean, a tool to determine the presence of bloated dependencies in Maven artifacts. We analyze 9,639 Java artifacts hosted on Maven Central, which include a total of 723,444 dependency relationships. Our key result is as follows: 2.7% of the dependencies directly declared are bloated, 15.4% of the inherited dependencies are bloated, and 57% of the transitive dependencies of the studied artifacts are bloated. In other words, it is feasible to reduce the number of dependencies of Maven artifacts to 1/4 of its current count. Our qualitative assessment with 30 notable open-source projects indicates that developers pay attention to their dependencies when they are notified of the problem. They are willing to remove bloated dependencies: 21/26 answered pull requests were accepted and merged by developers, removing 140 dependencies in total: 75 direct and 65 transitive.

When a developer pushes a change to an application’s codebase, a good practice is to have a test case specifying this behavioral change. Thanks to continuous integration (CI), the test is run on subsequent commits to check that they do no introduce a regression for that behavior. In this paper, we propose an approach that detects behavioral changes in commits. As input, it takes a program, its test suite, and a commit. Its output is a set of test methods that capture the behavioral difference between the pre-commit and post-commit versions of the program. We call our approach DCI (Detecting behavioral changes in CI). It works by generating variations of the existing test cases through (i) assertion amplification and (ii) a search-based exploration of the input space. We evaluate our approach on a curated set of 60 commits from 6 open source Java projects. To our knowledge, this is the first ever curated dataset of real-world behavioral changes. Our evaluation shows that DCI is able to generate test methods that detect behavioral changes. Our approach is fully automated and can be integrated into current development processes. The main limitations are that it targets unit tests and works on a relatively small fraction of commits. More specifically, DCI works on commits that have a unit test that already executes the modified code. In practice, from our benchmark projects, we found 15.29% of commits to meet the conditions required by DCI.

Ensuring the consistent usage of formatting conventions is an important aspect of modern software quality assurance. To do so, the source code of a project should be checked against the formatting conventions (or rules) adopted by its development team, and then the detected violations should be repaired if any. While the former task can be automatically done by format checkers implemented in linters, there is no satisfactory solution for the latter. Manually fixing formatting convention violations is a waste of developer time and code formatters do not take into account the conventions adopted and configured by developers for the used linter. In this paper, we present Styler, a tool dedicated to fixing formatting rule violations raised by format checkers using a machine learning approach. For a given project, Styler first generates training data by injecting violations of the project-specific rules in violation-free source code files. Then, it learns fixes by feeding long short-term memory neural networks with the training data encoded into token sequences. Finally, it predicts fixes for real formatting violations with the trained models. Currently, Styler supports a single checker, Checkstyle, which is a highly configurable and popular format checker for Java. In an empirical evaluation, Styler repaired 41% of 26,791 Checkstyle violations mined from 104 GitHub projects. Moreover, we compared Styler with the IntelliJ plugin CheckStyle-IDEA and the machine-learning-based code formatters Naturalize and CodeBuff. We found out that Styler fixes violations of a diverse set of Checkstyle rules (24/25 rules), generates smaller repairs in comparison to the other systems, and predicts repairs in seconds once trained on a project. Through a manual analysis, we identified cases in which Styler does not succeed to generate correct repairs, which can guide further improvements in Styler. Finally, the results suggest that Styler can be useful to help developers repair Checkstyle formatting violations.

Automatic Program Repair (APR) techniques can promisingly help reduce the cost of debugging. Many relevant APR techniques follow the generate-and-validate approach, that is, the faulty program is iteratively modified with different change operators and then validated with a test suite until a plausible patch is generated. In particular, Kali is a generate-and-validate technique developed to investigate the possibility of generating plausible patches by only removing code. Former studies show that indeed Kali successfully addressed several faults. This paper addresses the single and particular case of code-removal patches in automated program repair. We investigate the reasons and the scenarios that make their creation possible, and the relationship with patches implemented by developers. Our study reveals that code-removal patches are often insufficient to fix bugs, and proposes a comprehensive taxonomy of code-removal patches that provides evidence of the problems that may affect test suites, opening new opportunities for researchers in the field of automatic program repair.

In the literature, there is a rather clear segregation between manually written tests by developers and automatically generated ones. In this paper, we explore a third solution: to automatically improve existing test cases written by developers. We present the concept, design and implementation of a system called DSpot, that takes developer-written test cases as input (JUnit tests in Java) and synthesizes improved versions of them as output. Those test improvements are given back to developers as patches or pull requests, that can be directly integrated in the main branch of the test code base. We have evaluated DSpot in a deep, systematic manner over 40 real-world unit test classes from 10 notable and open-source software projects. We have amplified all test methods from those 40 unit test classes. In 26/40 cases, DSpot is able to automatically improve the test under study, by triggering new behaviors and adding new valuable assertions. Next, for ten projects under consideration, we have proposed a test improvement automatically synthesized by DSpot to the lead developers. In total, 13/19 proposed test improvements were accepted by the developers and merged into the main code base. This shows that DSpot is capable of automatically improving unit-tests in real-world, large scale Java software.

Metrics offer a practical approach to evaluate properties of domain-specific models. However, it is costly to develop and maintain measurement software for each domain-specific modeling language. In this paper, we present a model-driven and generative approach to measuring models. The approach is completely domain-independent and operationalized through a prototype that synthesizes a measurement infrastructure for a domain-specific modeling language. This model-driven measurement approach is model-driven from two viewpoints: (1) it measures models of a domain-specific modeling language; (2) it uses models as unique and consistent metric specifications, with respect to a metric specification metamodel which captures all the necessary concepts for model-driven specifications of metrics. The benefit from applying the approach is evaluated by four case studies. They indicate that this approach significantly eases the measurement activities of model-driven development processes.