Detecting Similar Applications with Collaborative Tagging

Abstract—Detecting similar applications are useful for var-ious purposes ranging from program comprehension, rapid prototyping, plagiarism detection, and many more. McMillan et al. have proposed a solution to detect similar applications based on common Java API usage patterns. Recently, collaborative tagging has impacted software development practices. Various sites allow users to give various tags to software systems. In this study, we would like to complement the study by McMillan et al. by leveraging another source of information aside from API usage patterns, namely software tags. We have performed a user study involving several participants and the results show that collaborative tagging is a promising source of information useful for detecting similar software applications. I

Towards generating transformation rules without examples for android API replacement

National Research Foundation (NRF) Singaporeauthors' own version</p

Cupid: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection

Duplicate bug report detection (DBRD) is a long-standing challenge in both academia and industry. Over the past decades, researchers have proposed various approaches to detect duplicate bug reports more accurately. With the recent advancement of deep learning, researchers have also proposed several approaches that leverage deep learning models to detect duplicate bug reports. A recent benchmarking study on DBRD also reveals that the performance of deep learning-based approaches is not always better than the traditional approaches. However, traditional approaches have limitations, e.g., they are usually based on the bag-of-words model, which cannot capture the semantics of bug reports. To address these aforementioned challenges, we seek to leverage state-of-the-art large language model to improve the performance of the traditional DBRD approach. In this paper, we propose an approach called Cupid, which combines the best-performing traditional DBRD approach REP with the state-of-the-art large language model ChatGPT. Specifically, we first leverage ChatGPT under the zero-shot setting to get essential information on bug reports. We then use the essential information as the input of REP to detect duplicate bug reports. We conducted an evaluation on comparing Cupid with three existing approaches on three datasets. The experimental results show that Cupid achieves new state-of-the-art results, reaching Recall Rate@10 scores ranging from 0.59 to 0.67 across all the datasets analyzed. Our work highlights the potential of combining large language models to improve the performance of software engineering tasks.Comment: Work in progres

CrossASR: Efficient differential testing of automatic speech recognition via text-to-speech

Lee Kuan Yew Fellowship, Singapore Management Universit

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development is an inherently collaborative process, where various stakeholders frequently express their opinions and emotions across diverse platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. Over the years, many tools have been proposed to aid in sentiment analysis, but accurately identifying the sentiments expressed in software engineering datasets remains challenging. Although fine-tuned smaller large language models (sLLMs) have shown potential in handling software engineering tasks, they struggle with the shortage of labeled data. With the emergence of bigger large language models (bLLMs), it is pertinent to investigate whether they can handle this challenge in the context of sentiment analysis for software engineering. In this work, we undertake a comprehensive empirical study using five established datasets. We assess the performance of three open-source bLLMs in both zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.Comment: Submitted to TOSE