{"title":"对软件开发动机的大规模调查","authors":"Idan Amit, Dror G. Feitelson","doi":"10.1016/j.jss.2025.112596","DOIUrl":null,"url":null,"abstract":"<div><h3>Context:</h3><div>Motivation is known to improve performance. In software development, in particular, there has been considerable interest in the motivation of contributors to open-source.</div></div><div><h3>Objective:</h3><div>We would like to predict motivation, in various settings. We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self-use, etc.), and evaluate their relative effect on motivation using supervised learning.</div></div><div><h3>Method:</h3><div>We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11-point scale. We also conducted a follow-up survey, enabling investigation of motivation improvement given improvement in motivators.</div></div><div><h3>Results:</h3><div>Predictive analysis — investigating how diverse motivators influence the probability of high motivation — provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict an increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation.</div></div><div><h3>Conclusions:</h3><div>All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivation improvement, and each motivator sheds light on a different aspect of motivation. Models based on multiple motivators predict <em>motivation improvement</em> with up to 94% accuracy, better than any single motivator.</div><div><em>Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board</em>.</div></div>","PeriodicalId":51099,"journal":{"name":"Journal of Systems and Software","volume":"231 ","pages":"Article 112596"},"PeriodicalIF":4.1000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A large scale survey of motivation in software development\",\"authors\":\"Idan Amit, Dror G. Feitelson\",\"doi\":\"10.1016/j.jss.2025.112596\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Context:</h3><div>Motivation is known to improve performance. In software development, in particular, there has been considerable interest in the motivation of contributors to open-source.</div></div><div><h3>Objective:</h3><div>We would like to predict motivation, in various settings. We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self-use, etc.), and evaluate their relative effect on motivation using supervised learning.</div></div><div><h3>Method:</h3><div>We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11-point scale. We also conducted a follow-up survey, enabling investigation of motivation improvement given improvement in motivators.</div></div><div><h3>Results:</h3><div>Predictive analysis — investigating how diverse motivators influence the probability of high motivation — provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict an increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation.</div></div><div><h3>Conclusions:</h3><div>All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivation improvement, and each motivator sheds light on a different aspect of motivation. Models based on multiple motivators predict <em>motivation improvement</em> with up to 94% accuracy, better than any single motivator.</div><div><em>Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board</em>.</div></div>\",\"PeriodicalId\":51099,\"journal\":{\"name\":\"Journal of Systems and Software\",\"volume\":\"231 \",\"pages\":\"Article 112596\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2025-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Systems and Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0164121225002651\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems and Software","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0164121225002651","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
A large scale survey of motivation in software development
Context:
Motivation is known to improve performance. In software development, in particular, there has been considerable interest in the motivation of contributors to open-source.
Objective:
We would like to predict motivation, in various settings. We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self-use, etc.), and evaluate their relative effect on motivation using supervised learning.
Method:
We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11-point scale. We also conducted a follow-up survey, enabling investigation of motivation improvement given improvement in motivators.
Results:
Predictive analysis — investigating how diverse motivators influence the probability of high motivation — provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict an increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation.
Conclusions:
All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivation improvement, and each motivator sheds light on a different aspect of motivation. Models based on multiple motivators predict motivation improvement with up to 94% accuracy, better than any single motivator.
Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
期刊介绍:
The Journal of Systems and Software publishes papers covering all aspects of software engineering and related hardware-software-systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to:
•Methods and tools for, and empirical studies on, software requirements, design, architecture, verification and validation, maintenance and evolution
•Agile, model-driven, service-oriented, open source and global software development
•Approaches for mobile, multiprocessing, real-time, distributed, cloud-based, dependable and virtualized systems
•Human factors and management concerns of software development
•Data management and big data issues of software systems
•Metrics and evaluation, data mining of software development resources
•Business and economic aspects of software development processes
The journal welcomes state-of-the-art surveys and reports of practical experience for all of these topics.