What Predicts Software Developers’ Productivity?
Abstract
Organizations have a variety of options to help their software developers become their most productive selves, from modifying office layouts, to investing in better tools, to cleaning up the source code. But which options will have the biggest impact? Drawing from the literature in software engineering and industrial/organizational psychology to identify factors that correlate with productivity, we designed a survey that asked 622 developers across 3 companies about these productivity factors and about self-rated productivity. Our results suggest that the factors that most strongly correlate with self-rated productivity were non-technical factors, such as job enthusiasm, peer support for new ideas, and receiving useful feedback about job performance. Compared to other knowledge workers, our results also suggest that software developers' self-rated productivity is more strongly related to task variety and ability to work remotely.
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One survey of developers found indications that developers tolerated, and in some cases valued, erroneous suggestions from the model.
“One survey of developers considered the use of AI to support translation between programming languages and found indications that developers tolerated, and in some cases valued, erroneous suggestions from the model.26”
From Measuring GitHub Copilot's Impact on Productivity (2024) · cites this paper as [26]
There is some evidence that acceptance rate (and indeed correctness) might not tell the whole story.
“There is some evidence that acceptance rate (and indeed correctness) might not tell the whole story.”
From Measuring GitHub Copilot's Impact on Productivity (2024) · cites this paper as [26]
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A Tale of Two Cities: Software Developers Working from Home during the COVID-19 Pandemic
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BackgroundAn Actionable Framework for Understanding and Improving Developer Experience
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“Previous research has also shown that doing work that feels impactful is important to developer productivity and satisfaction [3], [4].”
BackgroundResultInfluentialA Deep Dive into the Impact of COVID-19 on Software Development
Neto, P. A. D. M. S., Mannan, U. A., de Almeida, E. S., Nagappan, N., Lo, D., Kochhar, P. S., Gao, C., & Ahmed, I. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3088759 · Cited by 62 · see citing works
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BackgroundInfluentialCheng, R., Wang, R., Zimmermann, T., & Ford, D. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3651990 · Cited by 47 · see citing works
“Despite being recently invented, AI-powered code generation tools have already gained a huge amount of attention and popularity.”
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Enabling Good Work Habits in Software Developers through Reflective Goal-Setting
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“On productive days, developers generally manage to make a lot of progress on their tasks, are supported by their co-workers or other peers, and have a minimum amount of context switches [43], [46], [47].”
BackgroundNot All Dependencies are Equal: An Empirical Study on Production Dependencies in NPM
Latendresse, J., Mujahid, S., Costa, D., & Shihab, E. (2022) · International Conference on Automated Software Engineering · doi:10.1145/3551349.3556896 · Cited by 39
“While the use of open source packages significantly reduces development time and costs [18, 35, 45], it also exposes software applications to vulnerabilities.”
BackgroundFrom Copilot to Pilot: Towards AI Supported Software DevelopmentPreprint
Pudari, R., & Ernst, N. A. (2023) · arXiv.org · doi:10.48550/arxiv.2303.04142 · Cited by 37 · see citing works
“Human Input: 1 # sorting a list 2 arr = [2,4,1,3,7,5] AI-supported code completion tools suggestion: 1 n = len(arr) 2 for i in range(n): Consider our running example of performing a sorting operation on a list of numbers.”
BackgroundMethodRazzaq, A., Buckley, J., Lai, Q., Yu, T., & Botterweck, G. (2025) · ACM Computing Surveys · doi:10.1145/3687299 · Cited by 36 · see citing works
“This refers to a sense of control [S193] in the state of mind of the developer, where developers feel empowered over things such as alterations in a project, working conditions role, and related risks [S40, S57, S185].”
InfluentialOn the Untriviality of Trivial Packages: An Empirical Study of npm JavaScript Packages
Chowdhury, M. A. R., Abdalkareem, R., Shihab, E., & Adams, B. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3068901 · Cited by 33 · see citing works
“T HE use of third-party packages is becoming increasingly popular since it allows teams to reduce development time and costs and increase productivity [1], [2], [3].”
BackgroundWhat improves developer productivity at google? code quality
Lan, C., Murphy-Hill, E., Canning, M., Jaspan, C., Green, C., Knight, A., Zhang, N., & Kammer, E. (2022) · ESEC/SIGSOFT FSE · doi:10.1145/3540250.3558940 · Cited by 29 · see citing works
Self-citationHow Developers and Managers Define and Trade Productivity for Quality
Storey, M., Houck, B., & Zimmermann, T. (2021) · IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1145/3528579.3529177 · Cited by 26 · see citing works
“…code or delivering features quickly, but is also related totheir abilitytodeliverhigh-qualitywork [18],their abilitytowork without interruptions [12], and is impacted by their satisfaction and well-being [22], their happiness at work [7], and many other social and technical factors [15, 25].”
BackgroundMachuca-Villegas, L., Gasca-Hurtado, G., Puente, S. M., & Tamayo, L. M. R. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111408 · Cited by 22 · see citing works
The SPACE of developer productivity
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“We developed the SPACE framework to capture different dimensions of productivity, because without it, pervasive and potentially harmful myths about productivity may persist.”
MethodCorrective commit probability: a measure of the effort invested in bug fixing
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How Much Does AI Impact Development Speed? an Enterprise-Based Randomized Controlled Trial
Paradis, E., Grey, K., Madison, Q., Nam, D., Macvean, A., Meimand, V., Zhang, N., Ferrari-Church, B., & Chandra, S. (2025) · 2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00060 · Cited by 20 · see citing works
“Our framework was influenced by a recent systematic review of the different factors that influence AI adoption [44] and the literature on developer productivity [35].”
Mohamed, A., Assi, M., & Guizani, M. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3809494 · Cited by 19 · see citing works
“RQ3: Which dimensions of developer productivity are investigated and how do these dimensions map onto the SPACE framework?”
Measuring Secure Coding Practice and Culture: A Finger Pointing at the Moon is not the Moon
Ryan, I., Roedig, U., & Stol, K.-J. (2023) · International Conference on Software Engineering · doi:10.1109/icse48619.2023.00140 · Cited by 19 · see citing works
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MethodThe Impact of LLM-Assistants on Software Developer Productivity: A Systematic Literature Review
Mohamed, A., Assi, M., & Guizani, M. (2025) · arXiv.org · doi:10.48550/arxiv.2507.03156 · Cited by 19
“RQ3: Which dimensions of developer productivity are investigated and how do these dimensions map onto the SPACE framework?”
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Self-citationAn Empirical Study of Blockchain Repositories in GitHub
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Testing Time Limits in Screener Questions for Online Surveys with Programmers
Danilova, A., Horstmann, S. A., Smith, M., & Naiakshina, A. (2022) · International Conference on Software Engineering · doi:10.1145/3510003.3510223 · Cited by 13 · see citing works
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Assessing practitioner beliefs about software engineering
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“In another recent study, Murphy et al. showed non-technical factors (self-rated metric) were good predictors for productivity (Murphy-Hill et al. 2019).”
BackgroundMachuca-Villegas, L., Hurtado, G., Puente, S. M., & Tamayo, L. M. R. (2021) · Journal of universal computer science (Online) · doi:10.3897/jucs.65102 · Cited by 13
Breaking the Flow: A Study of Interruptions During Software Engineering Activities
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“Murphy-Hill et al. conducted a study across three different companies, using 48 questionnaire items as predictors to assess which factors better predict developers’ self-rated productivity [48].”
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Social and Human Factor Classification of Influence in Productivity in Software Development Teams
Machuca-Villegas, L., Gasca-Hurtado, G., Tamayo, L. M. R., & Puente, S. M. (2020) · European Conference on Software Process Improvement · doi:10.1007/978-3-030-56441-4_54 · Cited by 12
Just-In-Time TODO-Missed Commits Detection
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“Identifying the responsible developer for developing and maintaining software code has been a focal point in research, especially as developers’ productivity (Anvik et al. 2006; Mockus 2009; Murphy-Hill et al. 2019) and the analysis of code quality have garnered increasing attention in the field.”
Software Productivity in Practice: A Systematic Mapping Study
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Developer Productivity With and Without GitHub Copilot: A Longitudinal Mixed-Methods Case Study
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“Regardless of multiple approaches, no single metric fully captures productivity in software development (Jaspan & Sadowski, 2019).”
InfluentialLambiase, S., Catolino, G., Pecorelli, F., Tamburri, D., Palomba, F., van den Heuvel, W., & Ferrucci, F. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111878 · Cited by 9 · see citing works
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Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era
Kumar, S., Goel, D., Zimmermann, T., Houck, B., Ashok, B., & Bansal, C. (2025) · 2025 IEEE/ACM 47th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00007 · Cited by 8 · see citing works
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Thimmaraju, K., Rispens, S., & Ahn, G.-J. (2025) · Proceedings 2025 Workshop on Security Operation Center Operations and Construction · doi:10.14722/wosoc.2025.23002 · Cited by 8
Hidden in the Code: Visualizing True Developer Identities
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Follow Your Nose - Which Code Smells are Worth Chasing?Preprint
Amit, I., Ezra, N. B., & Feitelson, D. (2021) · arXiv.org · doi:10.48550/arxiv.2103.01861 · Cited by 7 · see citing works
“In turn, commits-per-developer is correlated with self-rated productivity [55] and team lead perception of productivity [60].”
BackgroundHow Robust are LLM-Generated Library Imports? An Empirical Study using Stack Overflow
Latendresse, J., Khatoonabadi, S., & Shihab, E. (2025) · arXiv.org · doi:10.48550/arxiv.2507.10818 · Cited by 6
“While open source software libraries significantly reduce development time and costs [1]–[3], depending on numerous libraries introduces complexity and potential dependency management challenges [4]–[6].”
Liang, A., Murphy-Hill, E. R., Weimer, W. R., & Huang, Y. (2024) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2024.3437355 · Cited by 5 · see citing works
“On one hand, corrections to the false discovery rate (FDR) are common in the software engineering literature (e.g. [96], [97], [98]).”
MethodSelf-citationTool for Measuring Productivity in Software Development Teams
Mota, J. S., Tives, H. A., & Canedo, E. (2021) · Inf. · doi:10.3390/info12100396 · Cited by 6
“0.829 Organization Innovative mindset the existence of a mindset that is always open to new ideas influences the productivity of software developers [55].”
BackgroundInfluentialThe Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study
Vella, A., & Blincoe, K. (2026) · arXiv.org · doi:10.48550/arxiv.2605.23135 · Cited by 5
“While productivity has been the focus of much research [17, 50], developer experience has not received much attention, despite prior research identifying it as a driver for productivity [27, 31, 44].”
Beller, M., Park, A., Nakad, K., Patel, A., Mohanty, S., Garberson, F., Verroken, H., Kennedy, A., Malone, I. G., Garg, V., et al. (2025) · SEIP@ICSE · doi:10.1145/3786583.3786901 · Cited by 5
“However, assessing the ultimate topline of internal development, measuring productivity accurately, remains challenging [4, 28, 47], as traditional metrics are often inaccurate and easily gameable.”
Hurtado, G., & Machuca-Villegas, L. (2024) · Computer Applications in Engineering Education · doi:10.1002/cae.22785 · Cited by 5 · see citing works
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Personal Productivity and Well-being - Chapter 2 of the 2021 New Future of Work ReportPreprint
Butler, J. L., Czerwinski, M., Iqbal, S. T., Jaffe, S., Nowak, K., Peloquin, E., & Yang, L. (2021) · arXiv.org · doi:10.48550/arxiv.2103.02524 · Cited by 5 · see citing works
Ganguly, K., Tahsin, N., Fuad, M. M. N., Ahammed, T., Asad, M., Huq, S. F., Rabbi, A. T. M. F., & Sakib, K. (2020) · arXiv.org · doi:10.48550/arxiv.2008.11636 · Cited by 5 · see citing works
“e change in their frequency of miscommunication, frequency of communication, meeting preparation, and feedback of work, as literature shows that these factors are related to employee productivity [8] [9] Fig. 3 is the visual representation of the change in team collaboration. During WFH, changes in the 4 factors relat…”
InfluentialThe Corrective Commit Probability Code Quality MetricPreprint
Amit, I., & Feitelson, D. (2020) · arXiv.org · doi:10.48550/arxiv.2007.10912 · Cited by 5 · see citing works
“is an output per time measure, and the inverse of time to complete a task, investigated in the classical work of Sackman et al. [91]. The number of commits is correlated with self-rated productivity [77] and team lead perception of productivity [81]. We chose commits as the output unit since a commit is a unit of work…”
BackgroundMethodEvolving with AI: A Longitudinal Analysis of Developer Logs
Sergeyuk, A., Huang, E., Karaeva, D., Serova, A., Golubev, Y., & Ahmed, I. (2026) · Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering · doi:10.1145/3744916.3787811 · Cited by 4
“While code output has been used as an indicator of developer activity [13, 24], we acknowledge that it captures only one specific facet of productivity — namely, code authoring — and does not account for other important activities such as debugging, de-sign, or collaboration.”
Namayala, P. P., Kondo, T. S., & Mselle, L. (2023) · International journal of human computer interactions · doi:10.1080/10447318.2023.2262270 · Cited by 4 · see citing works
“Nevertheless, AI has been an option for quick code production, automatic user behavior analysis, and providing personalized recommendations (Batarseh et al., 2020; Murphy-Hill et al., 2021).”
Investigando a Percepção de Feedback em Times de Desenvolvimento de Software: Um Estudo Inicial
de Souza, A. C. J., dos Santos, F., dos Santos, L. J., Belém, P., da Silva, S. D., Vivacqua, A., & de Mello, R. D. (2023) · Anais do VIII Workshop sobre Aspectos Sociais, Humanos e Econômicos de Software (WASHES 2023) · doi:10.5753/washes.2023.230072 · Cited by 4 · see citing works
“Murphy-Hill et al. (2019) e Šteinberga e Šmite (2011) sugerem que fatores não técnicos como o feedback promovem o desempenho e satisfação no trabalho e aumentam a qualidade do código.”
Background“There and Back Again?” On the Influence of Software Community Dispersion Over Productivity
Lambiase, S., Catolino, G., Pecorelli, F., Tamburri, D., Palomba, F., van den Heuvel, W., & Ferrucci, F. (2022) · EUROMICRO Conference on Software Engineering and Advanced Applications · doi:10.1109/seaa56994.2022.00035 · Cited by 4 · see citing works
“Moving the attention to which factors influence productivity, we discovered that several researchers focused their attention on this aspect [8]–[13].”
BackgroundMethodInfluentialRestrepo-Tamayo, L. M., Gasca-Hurtado, G., Machuca-Villegas, L., & Morillo-Puente, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320419 · Cited by 3 · see citing works
How do copilot suggestions impact developers’ frustration and productivity?
Guglielmi, E., Arnoudova, V., Bavota, G., Oliveto, R., & Scalabrino, S. (2025) · Empirical Software Engineering · doi:10.1007/s10664-026-10965-6 · Cited by 3
“Interruptions can increase stress, frustration, time pressure, and the effort required to complete core cognitive tasks [39], [40].”
No silver bullets: Why understanding software cycle time is messy, not magic
Flournoy, J., Lee, C. S., Wu, M., & Hicks, C. M. (2025) · Empirical Software Engineering · doi:10.1007/s10664-025-10735-w · Cited by 3 · see citing works
“Developers whose teams use metrics generally see those metrics as helpful, and developers who report agreement which team-level metrics are measured tend to report higher perceived productivity (C. Hicks, Lee, and Ramsey 2023; C. M. Hicks, Lee, and Ramsey 2024).”
InfluentialChallenges and Opportunities for Survey Research in the Age of Generative AI: An Experience Report
Meem, F. N., Smith, J., & Johnson, B. (2024) · IEEE Symposium on Visual Languages / Human-Centric Computing Languages and Environments · doi:10.1109/vl/hcc60511.2024.00066 · Cited by 3 · see citing works
“Prior work suggests that respondents who take less than 10 seconds per question may not be giving due consideration to provide useful or accurate responses [9].”
BackgroundMethodWeak Programmers Need Not Apply, LLMs Welcome! Survey Screening in the AI Era
Ryan, I., Roedig, U., & Stol, K.-J. (2026) · Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering · doi:10.1145/3744916.3787789 · Cited by 2 · see citing works
“Ask respondent [45] Yes No No Helpful Ask the same thing twice [46] Yes Helpful NA Helpful Request specific response [7, 26, 33] Yes Helpful NA Helpful”
Ghanbari, H., Terimaa, T., & Koskinen, K. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112803 · Cited by 2 · see citing works
Cassee, N., & Feldt, R. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3799711 · Cited by 2
“Silberzahn et al. [57] report that 29 independent analysis teams, tasked with answering the same research question, employed a wide range of analytical methods and reached conflicting conclusions.”
Large-Scale Empirical Analysis of Continuous Fuzzing: Insights From 1 Million Fuzzing Sessions
Shirai, T., Nourry, O., Kashiwa, Y., Fujiwara, K., Kamei, Y., & Iida, H. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3683879 · Cited by 2
“With over 97% of commercial code making use of open-source software (OSS) [2], any vulnerability present in OSS has the potential to spread across the world.”
A large scale survey of motivation in software development
Amit, I., & Feitelson, D. (2025) · Journal of Systems and Software · doi:10.1016/j.jss.2025.112596 · Cited by 2 · see citing works
The Software Librarian: Python Package Insights for Copilot
Latendresse, J., Day, N., Khatoonabadi, S., & Shihab, E. (2025) · 2025 IEEE/ACM 47th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-companion66252.2025.00014 · Cited by 2 · see citing works
Shanto, M. H., Asaduzzaman, M., Mondal, M., & Chowdhury, S. (2025) · IEEE Working Conference on Mining Software Repositories · doi:10.1109/msr66628.2025.00055 · Cited by 2
“Modern software development has made the usage of third-party artifacts a pillar, allowing teams to increase productivity, cut expenses, and shorten development times [1], [2] [3].”
Motivation Research Using Labeling Functions
Amit, I., & Feitelson, D. (2024) · International Conference on Evaluation & Assessment in Software Engineering · doi:10.1145/3661167.3661224 · Cited by 2
“The questions used in the labeling functions validation are: • I regularly have a high level of motivation to contribute to the repository (based on [46]) • How many hours a week do you work on the repository (average)?”
BackgroundIn the Zone: An Analysis of the Music Practices of Remote Software Developers
Moster, M., Chandra, A., Chu, C. W. L., Liu, W., & Rodeghero, P. (2022) · International Symposium on Empirical Software Engineering and Measurement · doi:10.1145/3544902.3546638 · Cited by 2
“Additionally, Murphy-Hill et al. identified a list of productivity factors and identified a ranking system for use in determining which productivity factors companies should invest in for a quicker return [20].”
BackgroundCerqueira, L., Nunes, L., Malheiros, V., Guerra, R., Santana, B., Mendonça, M. G., & Santos, J. (2024) · International Conference on Enterprise Information Systems · doi:10.5220/0012676400003690 · Cited by 2
“Overall, these studies indicate that measuring work engagement and job satisfaction can help organizations to understand and predict productivity (Forsgren et al., 2021; Murphy-Hill et al., 2019; Storey et al., 2019).”
BackgroundUnderstanding Developer Productivity: Input-Output Perspectives Within the SPACE Framework
Tunggono, I., & Princes, E. (2025) · International Conference on Computing and Artificial Intelligence · doi:10.1109/iccai65301.2025.11279703 · Cited by 1 · see citing works
“C3 [35] Efficiency Dimension (EFFC) I rarely get disruptions in my work.”
Enhancing Task In-Progress Time Predictions through Affective and Personality Factors
Silva, L., Barreto, C. A. S., Lima, M., & Madeira, H. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3749984 · Cited by 1 · see citing works
“Non-technical factors are critical to self-rated productivity [67].”
Fostering Productive Open Source Systems: Understanding the Impact of Collaborator Sentiment
Lee, J., & Cho, K. (2025) · Syst. · doi:10.3390/systems13060445 · Cited by 1 · see citing works
“In another, the obtained survey results indicated that developers do not rank attitude or emotion among the top factors related to perceived productivity, contrasting with general knowledge workers, who do prioritize emotional factors [49].”
On the Evolution of Unused Dependencies in Java Project Releases: An Empirical Study
Suwanachote, N., Shakizada, Y., Kashiwa, Y., Lin, B., & Iida, H. (2025) · IEEE Working Conference on Mining Software Repositories · doi:10.1109/msr66628.2025.00059 · Cited by 1
Investigating the Perception of Feedback in Software Development Teams: An Initial Study
de Souza, A. P. C., dos Santos, F., dos Santos, L. J., Belém, P., da Silva, S. T. F., Vivacqua, A., & de Mello, R. D. (2024) · Braz. J. Inf. Syst. · doi:10.5753/isys.2024.3820 · Cited by 1 · see citing works
“[Murphy-Hill et al. 2019] investigated self-assessment and productivity factors among developers.”
Assessing Software Developer Productivity and Emotional State Using Biometrics
Park, K.-I. (2024) · IEEE International Conference on Software Maintenance and Evolution · doi:10.1109/icsme58944.2024.00086 · Cited by 1
“In addition, studies that attempt to measure a developer’s level of productivity focus primarily on the work environment and how a given project is managed [6] [7] rather than the intricacies of the code itself.”
A Large Scale Survey of Motivation in Software Development and Analysis of its Validity
Amit, I., & Feitelson, D. (2024) · arXiv.org · doi:10.48550/arxiv.2404.08303 · Cited by 1
“We measured productivity by commits per working day [65, 67].”
BackgroundMethodInfluentialHow do Copilot Suggestions Impact Developers' Frustration and Productivity?
Guglielmi, E., Arnoudova, V., Bavota, G., Oliveto, R., & Scalabrino, S. (2025) · arXiv · doi:10.48550/arxiv.2504.06808 · Cited by 1 · see citing works
“Interruptions can increase stress, frustration, time pressure, and the effort required to complete core cognitive tasks [39], [40].”
Two's a Crowd: Human and AI-Based Copresence for Developers with ADHD
Pimenova, V., Bernstein, S., Madan, S., Jain, D., & Potluri, V. (2026) · doi:10.48550/arxiv.2609.21254
“Effective collaboration and communication among developers are vital to the productivity and success of software development teams [25, 30, 60].”
Muradi, H., Rumadi, R., Priyambada, S. A., Munthe, C. C., Afra, D. I. N., Juliansyah, R., Puspita, G. C., Prakasa, E., & Prawara, B. (2026) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina72033.2026.11714635
“As digital traces became available, Murphy-Hill et al. [7] identified job enthusiasm, coworker support, and feedback as the strongest predictors of developer productivity, Drucker [8] framed knowledge-worker productivity as a defining management challenge, and Janiesch et al. [9] documented ML’s…”
Altork, A., Wiese, I., & Melegati, J. (2026) · Journal of Software Evolution and Process · doi:10.1002/smr.70162
“In this regard, a survey with 622 developers showed that self-rated productivity mostly correlates with non-technical factors, such as job enthusiasm, peer support for new ideas, and receiving useful feedback about job performance [23].”
Bad Smells of Communication in Multidisciplinary Agile Teams of a Remote Software Project
Lobato, C., Portela, C., Souza, M., Palomino, P., Lisbôa, R., & Cordeiro, T. (2026) · Anais do XI Workshop sobre Aspectos Sociais, Humanos e Econômicos de Software (WASHES 2026) · doi:10.5753/washes.2026.20956
Gudimetla, S., Gangula, S., Adinarayana, R., Challa, C., Vunnam, S., & Buhyavarapu, S. (2026) · 2026 International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11542057
“These dimensions were identified through semi-structured interviews with 32 senior engineers, analysis of 450 code review interactions, and alignment with established software engineering competency frameworks [22, 28, 32].”
Borowa, K., Rasztabiga, B., Soroka, H., Tymoftyjewicz, M., & de Almeida, R. R. (2026) · SEIP@ICSE · doi:10.1145/3786583.3786884
“Later, Murphy-hill et al. [24] found that the factor most strongly correlating with productivity was “I am enthusiastic about my job.”
Gonçalves, V. F., Santo, J. P. T. E., Silva, G. F., Tome, C. F. A., Vasconcellos, J. F. A., & Matsubara, P. (2026) · SEET@ICSE · doi:10.1145/3786580.3786993
“…from a large multi-company study shows that the strongest predictors of perceived developer productivity are predominantly social and communicative: high-quality incoming information (e.g., clear bug reports), rapid issue resolution, peer support for new ideas, and useful performance feedback [25].”
Promoting social and human factors through a gamified automotive software development environment
Gasca-Hurtado, G., Muñoz, M., & Sameh, S. (2024) · J. Softw. Evol. Process. · doi:10.1002/smr.2704
“19 Moreover, software developers in the automotive industry must work effectively with their stakeholders to achieve successful products, in which different human aspects will impact their software development processes.”
Systematic Literature Review on IT Strategic Workspaces: Approach to Develop an Ideal Environment
Putra, A. A., Fusanto, P. F., Valonika, N., Anggreainy, M. S., & Kurniawan, A. (2023) · IEEE International Conference on Computing, Engineering and Design (ICCED) · doi:10.1109/icced60214.2023.10425467
“Attitude and personality that upholds humanity aspects is the ideal standard for any workplace in this modem industry [32], [43], Work discipline is a form of self-control from employees that shows their seriousness in doing work [13], [35], The application of work discipline will have an impact on…”
BackgroundBildirici, F., Karakus, M., & Şentürk, G. S. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108201
Sweat the Details: Exercise Breaks and AI Assistance in Code Summarization
Wei, A., Santiesteban, P., & Weimer, W. (2026) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2026.3716079
Software Development Teams: Factors Influencing their Productivity
Bollati, V., Gaona, G., Lima, P. B., & Pletsch, L. C. (2023) · Proceedings of the LACCEI international multi-conference for engineering, education and technology · doi:10.18687/laccei2023.1.1.1561
“Organizational [26], [28], [35], [36], [42]–[44], [49], [56] Organizational Support Team management Personnel Management Individual training management Characteristics of leadership Characteristics of the projects”
BackgroundMedvedev, A., & Medvedev, A. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202340301030
“People may have a specialty, but they are expected to act in different ways, demonstrating what are often referred to as "T-shaped skills" [23].”
Background
- Authors
- Murphy-Hill, E., Jaspan, C., Sadowski, C., Shepherd, D., Phillips, M., Winter, C., Knight, A., Smith, E., & Jorde, M.
- Affiliations
- Google (United States) (US)
- National Instruments (United States) (US)
- Bloomberg (United States) (US)
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2021-03-01
- Volume
- 47
- Issue
- 3
- Pages
- 582-594
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- 2326-3881
- OpenAlex
- W2915232514
- Cited by
- 100
- References
- 0
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 20 · 2023: 13 · 2022: 11 · 2021: 17 · 2020: 5 · 2019: 3
- FWCI
- 11.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Software Engineering (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Social Sciences › Human Factors and Ergonomics › Employee Performance and Management
- Social Sciences › Psychology › Social Psychology › Team Dynamics and Performance
- Keywords
- non-technical factors, work enthusiasm, peer support, performance feedback, task variety, remote work, knowledge workers, work environment
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