Breaking the Flow: A Study of Interruptions During Software Engineering Activities
Open accessAbstract
In software engineering, interruptions during tasks can have significant implications for productivity and well-being. While previous studies have investigated the effect of interruptions on productivity, to the best of our knowledge, no prior work has yet distinguished the effect of different types of interruptions on software engineering activities.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Higher variations in HRV indicate greater frustration during task execution.
“Higher variations in HRV indicate greater frustration during task execution [6].”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as [6]
Section 4.2 (page unavailable) interprets increases in the HRV measures SDNN and RMSSD as indicating *decreased* stress, contrary to the claimed direction. The paper assesses stress rather than establishing a relationship between HRV and frustration. (LLM, conf=0.84)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredInterruptions significantly increase stress levels.
“Indeed, Ma et al. [6] showed that interruptions significantly increase stress levels, interrupt cognitive flow, and lengthen task completion times.”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as [6]
Section 4.3 reports significantly higher self-reported stress during in-person interruptions than during on-screen interruptions. The claim overgeneralizes that finding: the comparison is between interruption types, and the Conclusion reports that physiological measures indicated reduced stress during in-person interruptions. (LLM, conf=0.88)
Experimental — accuracy not yet measuredInterruptions interrupt cognitive flow.
“Indeed, Ma et al. [6] showed that interruptions significantly increase stress levels, interrupt cognitive flow, and lengthen task completion times.”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as [6]
The Introduction says some developers may be distracted by interruptions and have difficulty resuming work, which supports the idea of disrupted focus. It frames this as a possibility, however, rather than reporting a finding that interruptions disrupt cognitive flow. (LLM, conf=0.82)
Experimental — accuracy not yet measuredInterruptions lengthen task completion times.
“Indeed, Ma et al. [6] showed that interruptions significantly increase stress levels, interrupt cognitive flow, and lengthen task completion times.”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as [6]
The Code Comprehension section reports that two specific on-screen interruptions significantly increased time spent on a problem compared with no interruption. It also reports no significant effect for in-person interruptions, while the Code Review finding describes varied effects on task duration. The unqualified claim that interruptions lengthen task completion times is broader than these results. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe many suggestions continuously provided by AIDTS might be perceived as interruptions that require attention, evaluation, and action.
“On the other hand, the many suggestions continuously provided by the tool might be perceived as interruptions [6] that require attention, evaluation, and action.”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as [6]
A combination of in-person and on-screen interruptions influenced productivity during code review.
“and a combination of in-person and on-screen interruptions influenced productivity during code review.”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as Ma et al. [6]
Interruptions during code writing are associated with greater stress than other tasks.
“The study finds that interruptions during code writing are associated with greater stress than other tasks.”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as Ma et al. [6]
High dominance on screen interruptions significantly increased the time required for code comprehension.
“the study revealed that high dominance on screen interruptions significantly increased the time required for code comprehension”
From How do Copilot Suggestions Impact Developers' Frustration and Productivity? (2025) · cites this paper as Ma et al. [6]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Development and validation of brief measures of positive and negative affect: The PANAS scales.
Watson, D., Clark, L. A., & Tellegen, A. (1988) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.54.6.1063 · Cited by 32,433 · see citing works
Mattick, R. P., & Clarke, J. C. (1998) · Behaviour Research and Therapy · doi:10.1016/s0005-7967(97)10031-6 · Cited by 3,367 · see citing works
The Cognitive Failures Questionnaire (CFQ) and its correlates.
Broadbent, D., Cooper, P. F., Fitzgerald, P. H., & Parkes, K. (1982) · British Journal of Clinical Psychology · doi:10.1111/j.2044-8260.1982.tb01421.x · Cited by 2,967 · see citing works
AN ALGORITHM FOR THE ORGANIZATION OF INFORMATION
AdelsonVelskii, M. (1963) · Defense Technical Information Center (DTIC) · Cited by 783 · see citing works
Effects of Work Stress on Ambulatory Blood Pressure, Heart Rate, and Heart Rate Variability
Vrijkotte, T. G. M., van Doornen, L. J. P., & de Geus, E. J. C. (2000) · Hypertension · doi:10.1161/01.hyp.35.4.880 · Cited by 756 · see citing works
Attention, distraction, and cognitive control under load
Lavie, N. (2010) · Current Directions in Psychological Science · doi:10.1177/0963721410370295 · Cited by 673 · see citing works
E-mail as a source and symbol of stress
Barley, S. R., Meyerson, D. E., & Grodal, S. (2011) · Organization Science · doi:10.1287/orsc.1100.0573 · Cited by 637 · see citing works
Virtual Work: Bridging Research Clusters
Raghuram, S., Hill, N. S., Gibbs, J. L., & Maruping, L. M. (2019) · Academy of Management Annals · doi:10.5465/annals.2017.0020 · Cited by 426 · see citing works
Age and reading: The impact of distraction.
Connelly, S., Hasher, L., & Zacks, R. T. (1991) · Psychology and Aging · doi:10.1037//0882-7974.6.4.533 · Cited by 425 · see citing works
Human-computer interaction – Interact 84
(1986) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(86)90067-4 · Cited by 402 · see citing works
Age and reading: The impact of distraction.
(1991) · doi:10.1037/0882-7974.6.4.533 · Cited by 299 · see citing works
Sadowski, C., Söderberg, E., Church, L., Sipko, M., & Bacchelli, A. (2018) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1145/3183519.3183525 · Cited by 280 · see citing works
Using psycho-physiological measures to assess task difficulty in software development
Fritz, T., Begel, A., Müller, S. C., Yigit-Elliott, S., & Züger, M. (2014) · Proceedings of the 36th International Conference on Software Engineering · doi:10.1145/2568225.2568266 · Cited by 262 · see citing works
What happens when software developers are (un)happy
Graziotin, D., Fagerholm, F., Wang, X., & Abrahamsson, P. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.02.041 · Cited by 253 · see citing works
Regalia, G., Onorati, F., Lai, M., Caborni, C., & Picard, R. W. (2019) · Epilepsy Research · doi:10.1016/j.eplepsyres.2019.02.007 · Cited by 240 · see citing works
Schuurmans, A. A. T., de Looff, P., Nijhof, K. S., Rosada, C., Scholte, R. H. J., Popma, A., & Otten, R. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01648-w · Cited by 210 · see citing works
Graziotin, D., Wang, X., & Abrahamsson, P. (2014) · PeerJ · doi:10.7717/peerj.289 · Cited by 206 · see citing works
Chow, P. I., Fua, K., Huang, Y., Bonelli, W., Xiong, H., Barnes, L. E., & Teachman, B. A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6820 · Cited by 172 · see citing works
Self-citationProceedings of the 2006 20th anniversary conference on Computer supported cooperative work
HINDS, P. J., & Martín, D. (2006) · doi:10.1145/1180875 · Cited by 170 · see citing works
The Work Life of Developers: Activities, Switches and Perceived Productivity
Meyer, A. N., Barton, L. E., Murphy, G. C., Zimmermann, T., & Fritz, T. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2017.2656886 · Cited by 170 · see citing works
The SPACE of Developer Productivity: There's more to it than you think.
Forsgren, N., Storey, M.-A., Maddila, C., Zimmermann, T., Houck, B., & Butler, J. (2021) · Queue · doi:10.1145/3454122.3454124 · Cited by 164 · see citing works
Working from home during COVID-19: A study of the interruption landscape.
Leroy, S., Schmidt, A. M., & Madjar, N. (2021) · Journal of Applied Psychology · doi:10.1037/apl0000972 · Cited by 146 · see citing works
Heart rate variability related to effort at work
Uusitalo, A. L. T., Mets, T., Martinmäki, K., Mauno, S., Kinnunen, U. M., & Rusko, H. K. (2011) · Applied Ergonomics · doi:10.1016/j.apergo.2011.01.005 · Cited by 119 · see citing works
Umair, M., Chalabianloo, N., Sas, C., & Ersoy, C. (2021) · IEEE Access · doi:10.1109/access.2021.3052131 · Cited by 116 · see citing works
The perception of work stressors is related to reduced parasympathetic activity
Clays, E., Bacquer, D. D., Crasset, V., Kittel, F., Smet, P. D., Kornitzer, M., Karasek, R. A., & Backer, G. D. (2010) · International Archives of Occupational and Environmental Health · doi:10.1007/s00420-010-0537-z · Cited by 113 · see citing works
Graziotin, D., Wang, X., & Abrahamsson, P. (2014) · Journal of Software Evolution and Process · doi:10.1002/smr.1673 · Cited by 111 · see citing works
Assessing social anxiety using gps trajectories and point-of-interest data
Huang, Y., Xiong, H., Leach, K., Zhang, Y., Chow, P., Fua, K., Teachman, B. A., & Barnes, L. E. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971761 · Cited by 109 · see citing works
Self-citationKang, M. G., Koh, S. B., Cha, B. S., Park, J. K., Woo, J., & Chang, S. J. (2004) · Yonsei Medical Journal · doi:10.3349/ymj.2004.45.5.838 · Cited by 108 · see citing works
What Predicts Software Developers’ Productivity?
Murphy-Hill, E., Jaspan, C., Sadowski, C., Shepherd, D., Phillips, M., Winter, C., Knight, A., Smith, E., & Jorde, M. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2900308 · Cited by 100 · cited 2× · see citing works
“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].”
BackgroundMethodHintsanen, M., Elovainio, M. J., Puttonen, S., Mika Kivimaki, B., Koskinen, T., Raitakari, O. T., & Keltikangas‐Järvinen, L. (2007) · International Journal of Behavioral Medicine · doi:10.1007/bf03002994 · Cited by 94 · see citing works
Concurrent Search and Insertion in AVL Trees
Ellis, E. (1980) · IEEE Transactions on Computers · doi:10.1109/tc.1980.1675680 · Cited by 93 · see citing works
Sin, N. L., Sloan, R. P., McKinley, P. S., & Almeida, D. M. (2016) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000306 · Cited by 80 · see citing works
EmotionSense: Emotion Recognition Based on Wearable Wristband
Zhao, B., Wang, Z., Yu, Z., et al. (2018) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld.2018.00091 · Cited by 72 · see citing works
Recognizing developers' emotions while programming
Girardi, D., Novielli, N., Fucci, D., & Lanubile, F. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380374 · Cited by 71 · see citing works
Punita, P., Saranya, K., & Kumar, S. S. (2016) · National Journal of Physiology Pharmacy and Pharmacology · doi:10.5455/njppp.2016.6.0102325042016 · Cited by 44 · see citing works
Stuyck, H., Costa, L. D., Axel Cleeremans, B., & den Bussche, E. V. (2022) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2022.10.003 · Cited by 44 · see citing works
Krueger, R., Huang, Y., Liu, X., Santander, T., Weimer, W. R., & Leach, K. (2020) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3377811.3380348 · Cited by 40 · see citing works
Self-citationHuang, Y., Leach, K., Sharafi, Z., McKay, N., Santander, T., & Weimer, W. R. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409681 · Cited by 38 · see citing works
Self-citationDistilling Neural Representations of Data Structure Manipulation using fMRI and fNIRS
Huang, Y., Liu, X., Krueger, R., Santander, T., Hu, X., Leach, K., & Weimer, W. R. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2019.00053 · Cited by 37 · see citing works
Self-citationSocial and competition stress detection with wristband physiological signals
Sevil, M., Hajizadeh, I., Samadi, S., Feng, J., Lazaro, C., Frantz, N., Xia, Y., Brandt, R., Maloney, Z., & Çınar, A. (2017) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2017.7936002 · Cited by 32 · see citing works
Mind the Gap: On the Relationship Between Automatically Measured and Self-Reported Productivity
Beller, M., Orgovan, V., Buja, S., & Zimmermann, T. (2021) · IEEE Software · doi:10.1109/ms.2020.3048200 · Cited by 16 · see citing works
A study on task management system
Jyothi, N. S., & Parkavi, A. (2016) · doi:10.1109/rains.2016.7764421 · Cited by 14 · see citing works
Emotional state and cognitive load analysis using features from BVP and SC sensors
Gökay, R., Maşazade, E., Aydın, Ç., & Barkana, D. E. (2015) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi.2015.7295805 · Cited by 12 · see citing works
Is it a good time to survey you? Cognitive load classification from blood volume pulse
Lisowska, A., Szymon Wilk, A., & Mor Peleg, B. (2021) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms52027.2021.00061 · Cited by 4 · see citing works
Abad, Z. S. H., Karras, O., Schneider, K. A., Barker, K. E., & Bauer, M. (2018) · EasyChair preprint · doi:10.29007/464w · Cited by 4 · see citing works
Abad, Z. S. H., Noaeen, M., Zowghi, D., Far, B. H., & Barker, K. (2018) · Proceedings of the 22nd International Conference on Evaluation and Assessment in Software Engineering 2018 · doi:10.1145/3210459.3214170
Flagged first, then by citations
Research works printed but not linked: 13
- Zahra Shakeri Hossein Abad, Guenther Ruhe, and Mike Bauer. 2017. Task Interruptions in Requirements Engineering: Reality versus Perceptions! Requirements Engineering Conference (RE), 2017 IEEE 25th International. IEEE, 6--15.
- Zahra Shakeri Hossein Abad, Guenther Ruhe, and Mike Bauer. 2017. Understanding Task Interruptions in Service Oriented Software Development Projects: An Exploratory Study. Proceedings of the 4th International Workshop on Software Engineering Research and Industrial Practice (SER&IP '17), 34--40.
- Zahra Shakeri Hossein Abad, Alex Shymka, Jenny Le, Noor Hammad, and Guenther Ruhe. 2017. A Visual Narrative Path from Switching to Resuming a Requirements Engineering Task. Requirements Engineering Conference (RE), 2017 IEEE 25th International, 442--447.
- B. P. Bailey, J. A. Konstan, and J. V. Carlis. 2001. The effects of interruptions on task performance, annoyance, and anxiety in the user interface. Int. Conf. Human-Comput. Interaction, 593--601.
- Alexander Bick, Adam Blandin, and Karel Mertens. 2020. Work from home after the COVID-19 outbreak. Psychological Assessment (2020).
- T. Chandola, A. Britton, E. Brunner, H. Hemingway, M. Malik, M. Kumari, E. Badrick, M. Kivimaki, and M Marmot. 2008. Work stress and coronary heart disease: what are the mechanisms? European heart journal 29, 5 (2008), 640--648.
- Huei-Yen Winnie Chen, Liberty Hoekstra-Atwood, and Birsen Donmez. 2018. Voluntary- and Involuntary-Distraction Engagement: An Exploratory Study of Individual Differences. Epub 60, 4 (2018), 533--541.
- 10.2196/jmir.6820 · DOI · Paper page
- C.K. Endukuru and S. Tripathi. 2016. Evaluation of cardiac responses to stress in healthy individuals- a non invasive evaluation by heart rate variability and stroop test. Int J Sci Res 5 (2016), 286--289.
- Yi-Jen Lin and Frank W. Wicker. 2007. A comparison of the effects of thought suppression, distraction and concentration. Behaviour Research and Therapy 45 (2007).
- G. Mark, D. Gudith, and U. Klocke. 2008. The cost of interrupted work : More speed and stress. SIGCHI Conf. Human Factors Comput, 107--110.
- A. Meyer E. T. Barr C. Bird and T. Zimmermann. 2019. Today was a good day: The daily life of software developers. Transactions on Software Engineering(TSE) (2019).
- M J Stones and A Kozma. 1989. Age, exercise, and coding performance. Psychol Aging 4, 2 (1989).
Cited by
Sorted by citations
“Get Me in the Groove”: a Mixed Methods Study on Supporting Adhd Professional Programmers
Newman, K., Snay, S., Endres, M., Parikh, M., & Begel, A. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00242 · Cited by 9 · see citing works
“Sustained focus and flow is essential for software productivity [27], [51].”
LOTUS Software to Process Wearable EmbracePlus Data
Fogarty, J. S. (2024) · Sensors · doi:10.3390/s24237462 · Cited by 8 · see citing works
“The diversity The previous literature shows promising outcomes for using EmbracePlus to measure stress or emotional arousal in realistic settings [2,3,10,12,14,19] or to enhance model accuracy for human activity recognition using statistical or machine learning methods [4,5,8,13,16].”
Peng, T., Yue, Q., Liang, Y., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-93052-y · Cited by 4 · see citing works
Bolzan, W., & Farias, K. (2025) · ACM Computing Surveys · doi:10.1145/3742899 · Cited by 3 · see citing works
“Previous empirical studies [49] reveal that interruptions during tasks can significantly affect productivity and well-being in a workday.”
Brandebusemeyer, C. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-companion66252.2025.00043 · Cited by 2 · see citing works
“In the software engineering context, such objective physiological measurements were mostly conducted in laboratory settings [19], [20], [21], [22], [23], [24], [25].”
Wearables to Measure Developer Experience at Work
Brandebusemeyer, C., Schimmer, T., & Bert Arnrich, A. (2025) · IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip66354.2025.00008 · Cited by 2 · see citing works
“Software developers’ emotions and perceived progress during software development tasks [18], [27] and the effect of different types of interruptions on software developers [28] have been examined in the research field with wearable devices.”
A Safety-Driven Interpretable Model for Vehicle Control With Impact on Traffic
Wang, X., Yang, Y., Shangguan, Y., et al. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3609205 · Cited by 1 · see citing works
“HRV is measured via the Empatica EmbracePlus wristband, a device validated for physiological monitoring [62], [63].”
How 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
“Indeed, we expect that they experience them as interruptions [6] that they can find bothering, especially when the recommendations are wrong.”
InfluentialMeyer, A. N., & Fritz, T. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3710934
Interruptions and Recovery: Leveraging Dynamic Code History in Development
Pham, V. T. T., Zhou, H., & Kelleher, C. L. (2025) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl-hcc65237.2025.00012
“Inter-ruptions can also impact emotional state, causing unpleasant feelings like stress or displeasure [7], [28], [38], [39].”
One More Prompt: LLM Coding is a Game You Can’t Quite Put Down - A Provocation
Beale, R. (2026) · Designing Interactive Systems Conference · doi:10.1145/3802974.3809412
Schulte, L., Zähl, P. M., Czarnecki, C., & Schneider, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29839-3_32
Kaya, A., Tokdemir, G., & Ertek, G. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-21174-3_27
- Authors
- Ma, Y., Huang, Y., & Leach, K.
- Affiliations
- Duke University (US)
- Vanderbilt University (US)
- Venue
- Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2024-04-12
- Pages
- 1-12
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4394746232
- Cited by
- 12
- References
- 47
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 1
- FWCI
- 13.52
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion, cc-by)
- Funders
- National Science Foundation (2211429)
- Vanderbilt University
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Personal Information Management and User Behavior
- Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- interruptions, task flow, productivity, developer well-being
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review