Abstract
Biometric sensor technology provides new opportunities to measure physiological changes in the human body that can be linked to various psychological processes. In software engineering, these biometric measurements can be used to gain insights on fundamental cognitive and emotional processes of software developers while they are working. In addition, biometric measures may be used to provide better and more instantaneous tool support for developers, for instance, by preventing defects from being introduced in the code or supporting focused work. In this chapter, we motivate the use of biometric measurements, introduce common types of biometric sensors and measures, discuss how to choose the right set of them and considerations for analyzing the collected data. We also discuss work in the area of software engineering and recommend further reading.
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References
Andreassi JL (2007) Psychophysiology: human behavior and physiological response. Lawrence Erlbaum Associates, Mahwah
Anthony L, Carrington P, Chu P, Kidd C, Lai J, Sears A (2011) Gesture dynamics: features sensitive to task difficulty and correlated with physiological sensors. Stress 1418(360):312–316
Ayres P (2001) Systematic mathematical errors and cognitive load. Contemp Educ Psychol 26(2):227–248
Bailey BP, Konstan JA, Carlis JV (2001) The effects of interruptions on task performance, annoyance, and anxiety in the user interface. In: Proceedings of interact, vol 1, pp 593–601
Bednarik R, Tukiainen M (2006) An eye-tracking methodology for characterizing program comprehension processes. In: Proceedings of the symposium on eye tracking research and applications, pp 125–132
Begel A, Vrzakova H (2018) Eye movements in code review. In: Proceedings of the workshop on eye movements in programming (EMIP ’18). ACM, New York, pp 5:1–5:5. https://doi.org/10.1145/3216723.3216727
Brookings JB, Wilson GF, Swain CR (1996) Psychophysiological responses to changes in workload during simulated air traffic control. Biol Psychol Psychophysiol Workload 42(3):361–377
Busjahn T, Bednarik R, Begel A, Crosby M, Paterson JH, Schulte C, Sharif B, Tamm S (2015) Eye movements in code reading: relaxing the linear order. In: 2015 IEEE 23rd international conference on program comprehension, pp 255–265. https://doi.org/10.1109/ICPC.2015.36
Cacioppo J, Tassinary LG, Berntson GG (2007) The handbook of psychophysiology. Cambridge University, Cambridge
Carniglia E, Caputi M, Manfredi V, Zambarbieri D, Pessa E (2012) The influence of emotional picture thematic content on exploratory eye movements. J Eye Mov Res 5(4):1–9
Chandrika KR, Amudha J, Sudarsan SD (2017) Recognizing eye tracking traits for source code review. In: 2017 22nd IEEE international conference on emerging technologies and factory automation (ETFA), pp 1–8. https://doi.org/10.1109/ETFA.2017.8247637
Chanel G, Rebetez C, Bétrancourt M, Pun T (2008) Boredom, engagement and anxiety as indicators for adaptation to difficulty in games. In: Proceedings of the 12th international conference on entertainment and media in the Ubiquitous Era, pp 13–17. https://doi.org/10.1145/1457199.1457203. http://doi.acm.org/10.1145/1457199.1457203
Crk I, Kluthe T, Stefik A (2015) Understanding programming expertise: an empirical study of phasic brain wave changes. ACM Trans Comput-Hum Interact 23(1):2:1–2:29. https://doi.org/10.1145/2829945
Crosby ME, Stelovsky J (1990) How do we read algorithms? A case study. Computer 23(1):25–35. https://doi.org/10.1109/2.48797
Czerwinski M, Cutrell E, Horvitz E (2000) Instant messaging: effects of relevance and timing. In: People and computers XIV: proceedings of HCI, British Computer Society, vol 2, pp 71–76
Das R, Chatterjee D, Das D, Sinharay A, Sinha A (2014) Cognitive load measurement—a methodology to compare low cost commercial EEG devices. In: 2014 International conference on advances in computing, communications and informatics (ICACCI), pp 1188–1194. https://doi.org/10.1109/ICACCI.2014.6968528
Doehring DG (1957) The relation between manifest anxiety and rate of eyeblink in a stress situation. Technical report, Central Institute for the Deaf, St Louis
Drachen A, Nacke LE, Yannakakis G, Pedersen AL (2010) Correlation between heart rate, electrodermal activity and player experience in first-person shooter games. In: Proceedings of the 5th symposium on video games, pp 49–54
Fakhoury S, Ma Y, Arnaoudova V, Adesope O (2018) The effect of poor source code lexicon and readability on developers’ cognitive load. In: Proceedings of the 26th conference on program comprehension (ICPC ’18). ACM, New York, pp 286–296. https://doi.org/10.1145/3196321.3196347
Floyd B, Santander T, Weimer W (2017) Decoding the representation of code in the brain: an fMRI study of code review and expertise. In: 2017 IEEE/ACM 39th international conference on software engineering (ICSE), pp 175–186. https://doi.org/10.1109/ICSE.2017.24
Freeman GL (1940) A method of inducing frustration in human subjects and its influence upon palmar skin resistance. Am J Psychol 53(1):117–120
Fritz T, Müller SC (2016) Leveraging biometric data to boost software developer productivity. In: 2016 IEEE 23rd international conference on software analysis, evolution, and reengineering (SANER), vol 5, pp 66–77
Fritz T, Begel A, Müller SC, Yigit-Elliott S, Züger M (2014) Using psycho-physiological measures to assess task difficulty in software development. In: Proceedings of the 36th international conference on software engineering (ACM, ICSE 2014), pp 402–413. https://doi.org/10.1145/2568225.2568266
Girardi D, Lanubile F, Novielli N (2017) Emotion detection using noninvasive low cost sensors. In: 2017 Seventh international conference on affective computing and intelligent interaction (ACII), pp 125–130. https://doi.org/10.1109/ACII.2017.8273589
Graziotin D, Fagerholm F, Wang X, Abrahamsson P (2018) What happens when software developers are (un)happy. J Syst Softw 140:32–47. https://doi.org/10.1016/j.jss.2018.02.041
Haag A, Goronzy S, Schaich P, Williams J (2004) Emotion recognition using bio-sensors: first steps towards an automatic system. Affect Dialogue Syst Lect Notes Comput Sci 3068:36–48
Haapalainen E, Kim S, Forlizzi JF, Dey AK (2010) Psycho-physiological measures for assessing cognitive load. In: Proceedings of the 12th international conference on ubiquitous computing, pp 301–310
Ikehara CS, Crosby ME (2005) Assessing cognitive load with physiological sensors. In: Proceedings of the 38th Hawaii international conference on system sciences, p 295a
Ikutani Y, Uwano H (2014) Brain activity measurement during program comprehension with NIRS. In: Proceedings of the international conference on software engineering, artificial intelligence, networking and parallel/distributed computing, pp 1–6
Iqbal ST, Zheng XS, Bailey BP (2004) Task-evoked pupillary response to mental workload in human-computer interaction. In: CHI ’04 extended abstracts on human factors in computing systems, pp 1477–1480
Jbara A, Feitelson DG (2017) How programmers read regular code: a controlled experiment using eye tracking. Empir Softw Eng 22(3):1440–1477. https://doi.org/10.1007/s10664-016-9477-x
Kapoor A, Burleson W, Picard RW (2007) Automatic prediction of frustration. Int J Hum Comput Stud 65(8):724–736
Kevic K, Walters BM, Shaffer TR, Sharif B, Shepherd DC, Fritz T (2015) Tracing software developers’ eyes and interactions for change tasks. In: Proceedings of the 2015 10th joint meeting on foundations of software engineering (ESEC/FSE 2015). ACM, New York, pp 202–213. https://doi.org/10.1145/2786805.2786864
Kosti MV, Georgiadis K, Adamos DA, Laskaris N, Spinellis D, Angelis L (2018) Towards an affordable brain computer interface for the assessment of programmers’ mental workload. Int J Hum Comput Stud 115:52–66. https://doi.org/10.1016/j.ijhcs.2018.03.002
Kramer AF (1991) Physiological metrics of mental workload: a review of recent progress. In: Multiple-task performance, pp 279–328
Kuznetsov NA, Shockley KD, Richardson MJ, Riley MA (2011) Effect of precision aiming on respiration and postural-respiratory synergy. Neurosci Lett 502(1):13–17
Levandovski R, Sasso E, Hidalgo MP (2013) Chronotype: a review of the advances, limits and applicability of the main instruments used in the literature to assess human phenotype. Trends Psychiatry Psychother 35(1):3–11
Li M, Lu BL (2009) Emotion classification based on gamma-band EEG. In: Conference proceedings of the annual international conference of the IEEE engineering in medicine and biology society, pp 1323–1326
Lin YP, Wang CH, Jung TP, Wu TL, Jeng SK, Duann JR, Chen JH (2010) EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng 57(7):1798–1806. https://doi.org/10.1109/TBME.2010.2048568
Mark G, Gudith D, Klocke U (2008) The cost of interrupted work: more speed and stress. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 107–110
McCraty R, Tomasino D (2006) Stress in health and diseases. In: Chap emotional stress, positive emotions, and psychophysiological coherence. Wiley-VCH, New York
McDuff D, Karlson A, Kapoor A, Roseway A, Czerwinski M (2012) AffectAura: an intelligent system for emotional memory. In: Proceedings of the 2012 ACM annual conference on human factors in computing systems, pp 849–858
Muldner K, Burleson W, VanLehn K (2010) “Yes!”: using tutor and sensor data to predict moments of delight during instructional activities. In: Proceedings of the 18th international conference on user modeling, adaptation, and personalization, pp 159–170
Müller SC, Fritz T (2015) Stuck and frustrated or in flow and happy: sensing developers’ emotions and progress. In: 2015 IEEE/ACM 37th IEEE international conference on software engineering, vol 1, pp 688–699. https://doi.org/10.1109/ICSE.2015.334
Müller SC, Fritz T (2016) Using (bio)metrics to predict code quality online. In: 2016 IEEE/ACM 38th international conference on software engineering (ICSE), pp 452–463. https://doi.org/10.1145/2884781.2884803
Nakagawa T, Kamei Y, Uwano H, Monden A, Matsumoto K, German DM (2014) Quantifying programmers’ mental workload during program comprehension based on cerebral blood flow measurement: a controlled experiment. In: Companion proceedings of international conference on software engineering
Peitek N, Siegmund J, Apel S, Kästner C, Parnin C, Bethmann A, Leich T, Saake G, Brechmann A (2018) A look into programmers’ heads. IEEE Trans Softw Eng. https://doi.org/10.1109/TSE.2018.2863303
Peper E, Harvey R, Lin IM, Tylova H, Moss D (2007) Is there more to blood volume pulse than heart rate variability, respiratory sinus arrhythmia, and cardiorespiratory synchrony? Biofeedback 35(2):54–61
Picard RW, Vyzas E, Healey J (2001) Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 23(10):1175–1191
Rani P, Sarkar N, Smith CA, Kirby LD (2004) Anxiety detecting robotic system—towards implicit human-robot collaboration. Robotica 22(1):85–95
Reuderink B, Mühl C, Poel M (2013) Valence, arousal and dominance in the EEG during game play. Int J Autom Adaptive Commun Syst 6(1):45–62
Richter P, Wagner T, Heger R, Weise G (1998) Psychophysiological analysis of mental load during driving on rural roads—a quasi-experimental field study. Ergonomics 41(5):593:609
Rodeghero P, McMillan C, McBurney PW, Bosch N, D’Mello S (2014) Improving automated source code summarization via an eye-tracking study of programmers. In: Proceedings of the 36th international conference on software engineering (ICSE 2014), pp 390–401. https://doi.org/10.1145/2568225.2568247
Sammler D, Grigutsch M, Fritz T, Koelsch S (2007) Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology 44:293–304. https://doi.org/10.1111/j.1469-8986.2007.00497.x
Schmidth S, Walach H (2000) Electrodermal activity (EDA)—state-of-the-art measurements and techniques for parapsychological purposes. J Parapsychol 64(2):139:163
Setz C, Arnrich B, Schumm J, Marca RL, Tröster G, Ehlert U (2010) Discriminating stress from cognitive load using a wearable EDA device. IEEE Trans Inf Technol Biomed 14(2):410–417
Sharif B, Shaffer T (2015) The use of eye tracking in software development. In: Schmorrow DD, Fidopiastis CM (eds) Foundations of augmented cognition. Springer, Cham, pp 807–816
Sharif B, Falcone M, Maletic JI (2012) An eye-tracking study on the role of scan time in finding source code defects. In: Proceedings of the symposium on eye tracking research and applications (ETRA ’12). ACM, New York, pp 381–384. https://doi.org/10.1145/2168556.2168642
Sharif B, Clark B, Maletic JI (2016) Studying developer gaze to empower software engineering research and practice. In: Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering. ACM, New York, pp 940–943. https://doi.org/10.1145/2950290.2983988
Sharif B, Meinken J, Shaffer T, Kagdi H (2017) Eye movements in software traceability link recovery. Empir Softw Eng 22(3):1063–1102. https://doi.org/10.1007/s10664-016-9486-9
Siegmund J, Kästner C, Apel S, Parnin C, Bethmann A, Leich T, Saake G, Brechmann A (2014) Understanding understanding source code with functional magnetic resonance imaging. In: Proceedings of the 36th international conference on software engineering, pp 378–389
Siegmund J, Peitek N, Parnin C, Apel S, Hofmeister J, Kästner C, Begel A, Bethmann A, Brechmann A (2017) Measuring neural efficiency of program comprehension. In: Proceedings of the 2017 11th joint meeting on foundations of software engineering (ESEC/FSE 2017). ACM, New York, pp 140–150. https://doi.org/10.1145/3106237.3106268
Steptoe A, Wardle J, Marmot M (2005) Positive affect and health-related neuroendocrine, cardiovascular, and inflammatory processes. Proc Natl Acad Sci 102(18):6508–6512
Sweller J (1988) Cognitive load during problem solving: effects on learning. Cogn Sci 12(2):257–285
Torii K, Matsumoto Ki, Nakakoji K, Takada Y, Takada S, Shima K (1999) Ginger2: an environment for computer-aided empirical software engineering. IEEE Trans Softw Eng 25(4):474–492
Uwano H, Nakamura M, Monden A, Matsumoto Ki (2006) Analyzing individual performance of source code review using reviewers’ eye movement. In: Proceedings of the symposium on eye tracking research and applications. ACM, San Diego, pp 133–140
Veltman J, Gaillard AW (1998) Physiological workload reactions to increasing levels of task difficulty. Ergonomics 41(5):656–669
Walter GF, Porges SW (1976) Heart rate and respiratory responses as a function of task difficulty: the use of discriminant analysis in the selection of psychologically sensitive physiological responses. Psychophysiology 13(6):563–571
Wilson GF (1992) Applied use of cardiac and respiration measures: practical considerations and precautions. Biol Psychol 34(2–3):163–178
Wrobel MR (2018) Applicability of emotion recognition and induction methods to study the behavior of programmers. Appl Sci 8(3):323. https://doi.org/10.3390/app8030323
Züger M, Müller SC, Meyer AN, Fritz T (2018) Sensing interruptibility in the office: a field study on the use of biometric and computer interaction sensors. In: Proceedings of the 2018 CHI conference on human factors in computing systems (CHI ’18). ACM, New York, pp 591:1–591:14. https://doi.org/10.1145/3173574.3174165
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Fagerholm, F., Fritz, T. (2020). Biometric Measurement in Software Engineering. In: Felderer, M., Travassos, G. (eds) Contemporary Empirical Methods in Software Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-32489-6_6
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