Opinion Mining for Software Development: A Systematic Literature Review

Abstract

Opinion mining, sometimes referred to as sentiment analysis, has gained increasing attention in software engineering (SE) studies. SE researchers have applied opinion mining techniques in various contexts, such as identifying developers’ emotions expressed in code comments and extracting users’ critics toward mobile apps. Given the large amount of relevant studies available, it can take considerable time for researchers and developers to figure out which approaches they can adopt in their own studies and what perils these approaches entail. We conducted a systematic literature review involving 185 papers. More specifically, we present (1) well-defined categories of opinion mining-related software development activities, (2) available opinion mining approaches, whether they are evaluated when adopted in other studies, and how their performance is compared, (3) available datasets for performance evaluation and tool customization, and (4) concerns or limitations SE researchers might need to take into account when applying/customizing these opinion mining techniques. The results of our study serve as references to choose suitable opinion mining tools for software development activities and provide critical insights for the further development of opinion mining techniques in the SE domain.

Fairness Testing: A Comprehensive Survey and Analysis of Trends

“…We define the keywords through a trial-and-error procedure [74] performed by the first two authors and a discussion among the first four authors. The final keywords used for searching include ("fair" OR "bias" OR "discrimination") AND ("software" OR "learning" OR "bug" OR "defect" OR "fault" OR "algorithm" OR "test" OR "detect" OR "evaluat").…”

Self-Admitted Technical Debt and comments’ polarity: an empirical study

“…Different authors have studied the sentiment and emotions expressed by developers (Mäntylä et al 2016;Murgia et al 2014;Novielli and Serebrenik 2019;Lin et al 2021). In particular, Mäntylä et al (2016) and Murgia et al (2014) studied emotions expressed in the context of issue reports, finding that there appears to be a link between issue priority and complexity and negative emotions present in issue reports.…”

“…Recently, a trend has emerged and consolidated to leverage sentiment analysis in empirical software engineering research (Novielli and Serebrenik 2019;Lin et al 2021). Murgia et al (2014) presented an early exploratory study of emotions in software artifacts.…”

A bibliometric analysis to explore sentiment analysis in the domain of social media research

“…Independent literature evaluations can do range in sophistication from a simple annotated bibliography to a thorough scienti c synthesis of the original literature. The focus of this article is the SLR, which is the more thorough method of performing a literature review than what is typically practised in standalone reviews (Lin et al, 2022;Okoli & Schabram, 2010). SLR is an appropriate research method for the current study because of the nature of the research questions (RQs), aiming to understand the trends and investigate prevailing gaps in the pertinent scienti c literature (Whittemore et al, 2014).…”