While having options could be liberating, too many options could lead to the sub-optimal solution being chosen. This is not an exception in the software engineering domain. Nowadays, API has become imperative in making software developers’ life easier. APIs help developers implement a function faster and more efficiently. However, given the large number of open-source libraries to choose from, choosing the right APIs is not a simple task. Previous studies on API recommendation leverage natural language (query) to identify which API would be suitable for the given task. However, these studies only consider one source of input, i.e., GitHub or Stack Overflow, independently. There are no existing approaches that utilize Stack Overflow to help generate better API sequence recommendations from queries obtained from GitHub. Therefore, in this study, we aim to provide a framework that could improve the result of the API sequence recommendation by leveraging information from Stack Overflow. In this work, we propose Picaso, which leverages contrastive learning to train a sentence embedding model and a cross-encoder model to build a classification model in order to find a semantically similar Stack Overflow post given an annotation (i.e., code comment). Subsequently, Picaso then uses the Stack Overflow’s title as a query expansion. Picaso then uses the extended queries to fine-tune a CodeBERT, resulting in an API sequence generation model. Based on our experiments, we found that incorporating the Stack Overflow information into CodeBERT would improve the performance of API sequence generation’s BLEU-4 score by 10.8%.

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such a high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on the evidence of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are adopted in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

Many implementations of research techniques that automatically repair software bugs target programs written in C. Work that targets Java often begins from or compares to direct translations of such techniques to a Java context. However, Java and C are very different languages, and Java should be studied to inform the construction of repair approaches to target it. We conduct a large-scale study of bug-fixing commits in Java projects, focusing on assumptions underlying common search-based repair approaches. We make observations that can be leveraged to guide high quality automatic software repair to target Java specifically, including common and uncommon statement modifications in human patches and the applicability of previously-proposed patch construction operators in the Java context.

Many fault localization techniques have been proposed to facilitate debugging activities. Most of them attempt to pinpoint the location of faults (i.e., localize faults) based on a set of failing and correct executions and expect debuggers to investigate a certain number of located program elements to find faults. These techniques thus assume that faults are localizable, i.e., only one or a few lines of code that are close to one another are responsible for each fault. However, in reality, are faults localizable? In this work, we investigate hundreds of real faults in several software systems, and find that many faults may not be localizable to a few lines of code and these include faults with high severity level.