Originally, GenProg was created to repair buggy programs written in the C programming language, launching a new discipline in Generate-and-Validate approach of Automated Program Repair (APR). Since then, a number of other tools has been published using a variety of repair approaches. Some of these still operate on programs written in C/C++, others on Java or even Python programs. In this work, a tool named GenProgJS is presented, which generates candidate patches for faulty JavaScript programs. The algorithm it uses is very similar to the genetic algorithm used in the original GenProg, hence the name. In addition to the traditional approach, solutions used in some more recent works were also incorporated, and JavaScript language-specific approaches were also taken into account when the tool was designed. To the best of our knowledge, the tool presented here is the first to apply GenProg's general generate-and-validate approach to JavaScript programs. We evaluate the method on the BugsJS bug database, where it successfully fixed 31 bugs in 6 open source Node.js projects. These bugs belong to 14 different categories showing the generic nature of the method. During the experiments, code transformations applied on the original source code are all traced, and an in-depth analysis of mutation operators and fine-grained changes are also presented. We share our findings with the APR research community and describe the difficulties and differences we faced while designed this JavaScript repair tool. The source code of GenProgJS is publicly available on Github, with a pre-configured Docker environment where it can easily be launched.

Numerous Deep Learning (DL)-based fault localization (FL) methods are developed with the aim of leveraging the code coverage matrix and failure vector to identify the connection between program elements and defects. The imbalanced data on which these approaches train their models poses a substantial challenge to the effectiveness of fault localization techniques. This study explores the stability of fault localization models in deep learning, specifically, their performance when trained repeatedly using the same input but varying random initializations. Using the Defect4J benchmark, we trained deep learning models (MLP, CNN, and RNN) independently and found that 86 cases resulted in (partly) consistent rankings among all five models and versions, while 621 exhibited varying outcomes, meaning that 90 % of the produced ranks were different in subsequent trainings. The models showed significant variability in ranking results, with maximum ranks sometimes five times that of the minimum. We also adapted the churn metric from DL research to evaluate models, confirming their instability. To improve stability, meta-parameter optimization, model simplification and resampling has been applied. Although some of these techniques proved effective, even with the improvements, the models remained insufficiently stable to produce reliable results.

The field of Automated Program Repair (APR) has received increasing attention in recent years both from the academic world and from leading IT companies. Its main goal is to repair software bugs automatically, thus reducing the cost of development and maintenance significantly. Recent works use state-of-the-art deep learning models to predict correct patches, for these teaching on a large amount of data is inevitable almost in every scenarios. Despite this, readily accessible data on the field is very scarce. To contribute to related research, we present FixJS, a dataset containing bug-fixing information of ~2 million commits. The commits were gathered from GitHub and processed locally to have both the buggy (before bug fixing commit) and fixed (after fix) version of the same program. We focused on JavaScript functions, as it is one of the most popular programming language globally and functions are first class objects there. The data includes more than 300,000 samples of such functions, including commit information, before/after states and 3 source code representations.

In industrial practice the clone-and-own strategy is often applied when in the pressure of high demand of customized features. The adoption of software product line (SPL) architecture is a large one time investment that affects both technical and organizational issues. The analysis of the feature structure is a crucial point in the SPL adoption process involving domain experts working at a higher level of abstraction and developers working directly on the program code. We propose automatic methods to extract feature-to-program links starting from very high level set of features provided by domain experts. For this purpose we combine call graph information with textual similarity between code and high level features. In addition, in depth understanding of the feature structure is supported by finding communities between programs and relating them to features. As features are originated from domain experts, community analysis reveals discrepancies between expert view and internal code structure. We found that communities correspond well to the high level features, with usually more than half of feature code located in specialized communities. We report experiments at two levels of features and more than 2000 Magic 4GL programs in an industrial SPL adoption project.