Opinion Mining for Software Development: A Systematic Literature Review

A. Perini Jacek Dąbrowski Emmanuel Letier + 1 lainnya

20 Januari 2022

App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

Ting Zhang I. Irsan Ferdian Thung + 1 lainnya

17 Oktober 2023

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. 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.

M. Fahmideh M. Akbar Javed Ali Khan + 2 lainnya

2 Mei 2023

Context Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &A forums. Objective We conducted an empirical study to analyze developers’ discussions on Q &A forums to gain insights into software development approaches in practice. Method We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings We identified 15 frequently mentioned software development approaches topics on Q &A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.

N. Cagiltay A. Soylu Fatih Gurcan + 1 lainnya

2022

The landscape of software engineering research has changed significantly from one year to the next in line with industrial needs and trends. Therefore, today’s research literature on software engineering has a rich and multidisciplinary content that includes a large number of studies; however, not many of them demonstrate a holistic view of the field. From this perspective, this study aimed to reveal a holistic view that reflects topics, trends, and trajectories in software engineering research by analyzing the majority of domain-specific articles published over the last 40 years. This study first presents an objective and systematic method for corpus creation through major publication sources in the field. A corpus was then created using this method, which includes 44 domain-specific conferences and journals and 57,174 articles published between 1980 and 2019. Next, this corpus was analyzed using an automated text-mining methodology based on a probabilistic topic-modeling approach. As a result of this analysis, 24 main topics were found. In addition, topical trends in the field were revealed. Finally, three main developmental stages of the field were identified as: the programming age, the software development age, and the software optimization age.

Elaine H. T. de Oliveira T. Conte W. Nakamura + 1 lainnya

16 Oktober 2023

The mobile app market has grown over the last decades. With the rise of app stores, users can easily choose an app from thousands, making them less tolerant of low-quality apps. More than ever, users are looking for apps that provide not only valuable functionalities but pleasurable experiences. Hence, User eXperience (UX) became the differential to stand out from competitors. By understanding what factors affect UX, practitioners could focus on factors that lead to positive UX while mitigating those that affect UX negatively. In this context, reviews from app stores emerged as a valuable source of information to investigate such factors. However, analyzing millions of reviews is costly and time-consuming. This paper presents UX-MAPPER, an approach to analyzing app store reviews and supporting practitioners in identifying factors affecting UX. We applied the Design Science Research method to design UX-MAPPER iteratively and grounded on a solid theoretical background. We performed exploratory studies to investigate the problem, a systematic mapping study to identify factors that affect UX, and an empirical study with 14 participants with experience in requirements engineering to determine the relevance and acceptance of our proposal from practitioners’ perspectives. The participants considered it useful to improve the quality of existing apps and explore the reviews of competing apps to identify functionalities and features that users are requesting, liking, or hating. They were also willing to use it when it became available, highlighting our proposal’s usefulness and relevance in software development.