Psycholinguistic analyses in software engineering text: A systematic mapping study

IF 4.3 2区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information and Software Technology Pub Date : 2025-10-13 DOI:10.1016/j.infsof.2025.107913

Amirali Sajadi , Kostadin Damevski , Preetha Chatterjee

{"title":"Psycholinguistic analyses in software engineering text: A systematic mapping study","authors":"Amirali Sajadi , Kostadin Damevski , Preetha Chatterjee","doi":"10.1016/j.infsof.2025.107913","DOIUrl":null,"url":null,"abstract":"<div><h3>Context:</h3><div>A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers’ psychological and emotional states. While large language models excel at text analysis, they often lack transparency and precision. Psycholinguistic tools like Linguistic Inquiry and Word Count (LIWC) offer clearer, interpretable insights into cognitive and emotional processes exhibited in text. Despite its wide use in SE research, no comprehensive mapping study of LIWC’s use has been conducted.</div></div><div><h3>Objective:</h3><div>We examine the importance of psycholinguistic tools, particularly LIWC, and provide a thorough analysis of its current and potential future applications in SE research.</div></div><div><h3>Methods:</h3><div>We conducted a systematic mapping study of six prominent databases, identifying 43 SE-related papers using LIWC. Our analysis focuses on five research questions: <em>RQ1. How was LIWC employed in SE studies, and for what purposes?, RQ2. What datasets were analyzed using LIWC?, RQ3: What Behavioral Software Engineering (BSE) concepts were studied using LIWC? RQ4: How often has LIWC been evaluated in SE research?, RQ5: What concerns were raised about adopting LIWC in SE?</em></div></div><div><h3>Results:</h3><div>Our findings reveal a wide range of applications, including analyzing team communication to detect developer emotions and personality, developing ML models to predict deleted Stack Overflow posts, and more recently comparing AI-generated and human-written text. LIWC has been primarily used with data from project management platforms (e.g., GitHub) and Q&A forums (e.g., Stack Overflow). Key BSE concepts include <em>Communication</em>, <em>Organizational Climate</em>, and <em>Positive Psychology</em>. 26 of 43 papers did not formally evaluate LIWC. Concerns were raised about some limitations, including difficulty handling SE-specific vocabulary.</div></div><div><h3>Conclusion:</h3><div>We highlight the potential of psycholinguistic tools and their limitations, and present new use cases for advancing research on human factors in SE (e.g., bias in human-LLM conversations).</div></div>","PeriodicalId":54983,"journal":{"name":"Information and Software Technology","volume":"189 ","pages":"Article 107913"},"PeriodicalIF":4.3000,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information and Software Technology","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950584925002526","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 0

Abstract

Context:

A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers’ psychological and emotional states. While large language models excel at text analysis, they often lack transparency and precision. Psycholinguistic tools like Linguistic Inquiry and Word Count (LIWC) offer clearer, interpretable insights into cognitive and emotional processes exhibited in text. Despite its wide use in SE research, no comprehensive mapping study of LIWC’s use has been conducted.

Objective:

We examine the importance of psycholinguistic tools, particularly LIWC, and provide a thorough analysis of its current and potential future applications in SE research.

Methods:

We conducted a systematic mapping study of six prominent databases, identifying 43 SE-related papers using LIWC. Our analysis focuses on five research questions: RQ1. How was LIWC employed in SE studies, and for what purposes?, RQ2. What datasets were analyzed using LIWC?, RQ3: What Behavioral Software Engineering (BSE) concepts were studied using LIWC? RQ4: How often has LIWC been evaluated in SE research?, RQ5: What concerns were raised about adopting LIWC in SE?

Results:

Our findings reveal a wide range of applications, including analyzing team communication to detect developer emotions and personality, developing ML models to predict deleted Stack Overflow posts, and more recently comparing AI-generated and human-written text. LIWC has been primarily used with data from project management platforms (e.g., GitHub) and Q&A forums (e.g., Stack Overflow). Key BSE concepts include Communication, Organizational Climate, and Positive Psychology. 26 of 43 papers did not formally evaluate LIWC. Concerns were raised about some limitations, including difficulty handling SE-specific vocabulary.

Conclusion:

We highlight the potential of psycholinguistic tools and their limitations, and present new use cases for advancing research on human factors in SE (e.g., bias in human-LLM conversations).

查看原文本刊更多论文

软件工程文本中的心理语言学分析：系统的映射研究

上下文：更深入地理解软件工程（SE）中的人为因素对于改进团队协作、决策和生产力是必不可少的。像代码审查和聊天这样的沟通渠道提供了对开发人员心理和情绪状态的洞察。虽然大型语言模型擅长文本分析，但它们往往缺乏透明度和准确性。像语言调查和字数统计（LIWC）这样的心理语言学工具为文本中表现出的认知和情感过程提供了更清晰、可解释的见解。尽管它在SE研究中得到了广泛的应用，但目前还没有对LIWC的使用进行全面的测绘研究。目的：研究心理语言学工具，特别是LIWC的重要性，并对其在SE研究中的现状和潜在的未来应用进行全面分析。方法：我们对6个知名数据库进行了系统的制图研究，利用LIWC识别了43篇se相关的论文。我们的分析集中在五个研究问题：RQ1。在SE研究中如何使用LIWC，目的是什么？, RQ2。使用LIWC分析了哪些数据集？， RQ3：使用LIWC研究了哪些行为软件工程（BSE）概念？RQ4：在SE研究中对LIWC进行评估的频率是多少？RQ5：在SE中采用LIWC引起了什么关注？结果：我们的研究结果揭示了广泛的应用，包括分析团队沟通以检测开发人员的情绪和个性，开发ML模型以预测已删除的Stack Overflow帖子，以及最近比较人工智能生成和人类编写的文本。LIWC主要用于项目管理平台（如GitHub）和问答论坛（如Stack Overflow）的数据。关键的BSE概念包括沟通、组织氛围和积极心理学。43篇论文中有26篇没有正式评估LIWC。对一些限制提出了担忧，包括处理se特定词汇的困难。结论：我们强调了心理语言学工具的潜力及其局限性，并提出了推进SE中人为因素研究的新用例（例如，人与法学硕士对话中的偏见）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Information and Software Technology 工程技术-计算机：软件工程

CiteScore

9.10

自引率

7.70%

发文量

164

审稿时长

9.6 weeks

期刊介绍： Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal''s scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include: • Software management, quality and metrics, • Software processes, • Software architecture, modelling, specification, design and programming • Functional and non-functional software requirements • Software testing and verification & validation • Empirical studies of all aspects of engineering and managing software development Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information. The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.