Related papers: Retrospective: Data Mining Static Code Attributes …
Static Code Analyzers (SCAs) have played a critical role in software quality assurance. However, SCAs with various static analysis techniques suffer from different levels of false positives and false negatives, thereby yielding the varying…
Over the past few years, deep learning methods have been applied for a wide range of Software Engineering (SE) tasks, including in particular for the important task of automatically predicting and localizing faults in software. With the…
Data science pipelines to train and evaluate models with machine learning may contain bugs just like any other code. Leakage between training and test data can lead to overestimating the model's accuracy during offline evaluations, possibly…
Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to classify these comments have been proposed. In this work, we address…
This paper explores the structure of research papers in software engineering. Using text mining, we study 35,391 software engineering (SE) papers from 34 leading SE venues over the last 25 years. These venues were divided, nearly evenly,…
Static source code analysis is a powerful tool for finding and fixing bugs when deployed properly; it is, however, all too easy to deploy it in a way that looks good superficially, but which misses important defects, shows many false…
Collecting quality data from software projects can be time-consuming and expensive. Hence, some researchers explore "unsupervised" approaches to quality prediction that does not require labelled data. An alternate technique is to use…
As any scientific discipline, the software engineering (SE) research community strives to contribute to the betterment of the target population of our research: software producers and consumers. We will only achieve this betterment if we…
Defect prediction is one of the most popular research topics due to its potential to minimize software quality assurance efforts. Existing approaches have examined defect prediction from various perspectives such as complexity and developer…
The Software Engineering (SE) community has been embracing the open science policy and encouraging researchers to disclose artifacts in their publications. However, the status and trends of artifact practice and quality remain unclear,…
Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide…
In recent years, defect prediction, one of the major software engineering problems, has been in the focus of researchers since it has a pivotal role in estimating software errors and faulty modules. Researchers with the goal of improving…
Background: Software development results in the production of various types of artifacts: source code, version control system metadata, bug reports, mailing list conversations, test data, etc. Empirical software engineering (ESE) has…
Background: Machine learning algorithms are widely used to predict defect prone software components. In this literature, computational experiments are the main means of evaluation, and the credibility of results depends on experimental…
Designing a static analysis is generally a substantial undertaking, requiring significant expertise in both program analysis and the domain of the program analysis, and significant development resources. As a result, most program analyses…
Being able to duplicate published research results is an important process of conducting research whether to build upon these findings or to compare with them. This process is called "replicability" when using the original authors'…
Knowledge-based systems reason over some knowledge base. Hence, an important issue for such systems is how to acquire the knowledge needed for their inference. This paper assesses active learning methods for acquiring knowledge for "static…
Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim and has become a key component in applications like fake news detection, claim validation, and argument search. However, while stance is easily…
Many factors could affect the number of citations to a paper. Citations have an important role in research policy and in measuring the excellence of research and researchers. This work is the first study in software engineering (SE) to…
Software quality is one of the essential aspects of a software. With increasing demand, software designs are becoming more complex, increasing the probability of software defects. Testers improve the quality of software by fixing defects.…