ABSTRACT
Code review is a critical step in modern software quality assurance, yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code,but no consensus understanding has emerged. Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behavioral, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias.
Supplemental Material
- J. G. Altonji and R. M. Blank. Race and gender in the labor market. Handbook of labor economics, 3 : 3143-3259, 1999.Google Scholar
- A. Bacchelli and C. Bird. Expectations, outcomes, and challenges of modern code review. In Proceedings of the 2013 international conference on software engineering, pages 712-721. IEEE Press, 2013.Google ScholarDigital Library
- T. Baum, H. Leßmann, and K. Schneider. The choice of code review process: A survey on the state of the practice. In International Conference on Product-Focused Software Process Improvement, pages 111-127. Springer, 2017.Google ScholarCross Ref
- L. Beckwith, D. Inman, K. Rector, and M. Burnett. On to the real world: Gender and self-eficacy in excel. In Proceeding of the 2007 Symposium on Visual Languages and Human-Centric Computing, pages 119-126. IEEE, 2007.Google ScholarDigital Library
- R. Bednarik. Expertise-dependent visual attention strategies develop over time during debugging with multiple code representations. International Journal of Human-Computer Studies, 70 ( 2 ): 143-155, Feb. 2012.Google ScholarDigital Library
- J. S. Beer, M. Stallen, M. V. Lombardo, K. Gonsalkorale, W. A. Cunningham, and J. W. Sherman. The quadruple process model approach to examining the neural underpinnings of prejudice. Neuroimage, 43 ( 4 ): 775-783, 2008.Google ScholarCross Ref
- A. Begel and H. Vrzakova. Eye movements in code review. In Proceedings of the Workshop on Eye Movements in Programming, 2018.Google ScholarDigital Library
- S. Beyer. Gender diferences in the accuracy of self-evaluations of performance. Journal of personality and social psychology, 59 ( 5 ): 960, 1990.Google Scholar
- A. Bosu and J. C. Carver. Impact of peer code review on peer impression formation: A survey. In Empirical Software Engineering and Measurement, 2013.Google ScholarCross Ref
- A. Bosu, M. Greiler, and C. Bird. Characteristics of useful code reviews: An empirical study at microsoft. In 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories, pages 146-156. IEEE, 2015.Google ScholarCross Ref
- T. Camp, W. DuBow, D. Levitt, L. J. Sax, V. Taylor, and C. Lewis. The new NSF requirement for broadening participation in computing (BPC) plans: Community advice and resources. In Computer Science Education, pages 332-333, 2019.Google Scholar
- L. F. Capretz and F. Ahmed. Why do we need personality diversity in software engineering ? ACM SIGSOFT Software Engineering Notes, 35 ( 2 ): 1-11, 2010.Google ScholarDigital Library
- J. Castelhano, I. C. Duarte, C. Ferreira, J. Duraes, H. Madeira, and M. CasteloBranco. The Role of the Insula in Intuitive Expert Bug Detection in Computer Code: An fMRI Study. Brain Imaging and Behavior, May 2018.Google Scholar
- Z. Cattaneo, G. Mattavelli, E. Platania, and C. Papagno. The role of the prefrontal cortex in controlling gender-stereotypical associations: a tms investigation. NeuroImage, 56 ( 3 ): 1839-1846, 2011.Google ScholarCross Ref
- A. M. Chekroud, J. A. Everett, H. Bridge, and M. Hewstone. A review of neuroimaging studies of race-related prejudice: does amygdala response reflect threat? Frontiers in Human Neuroscience, 8 : 179, 2014.Google ScholarCross Ref
- J. Cohen. 11 proven practices for more efective, eficient peer code review. https://www.ibm.com/developerworks/rational/library/11-provenpractices-for-peer-review/index.html, January 2011.Google Scholar
- J. Cohen, E. Brown, B. DuRette, and S. Teleki. Best kept secrets of peer code review. Smart Bear Somerville, 2006.Google Scholar
- W. A. Cunningham, J. J. Van Bavel, and I. R. Johnsen. Afective flexibility: evaluative processing goals shape amygdala activity. Psychological Science, 19 ( 2 ): 152-160, 2008.Google Scholar
- L. Dabbish, C. Stuart, J. Tsay, and J. Herbsleb. Social coding in GitHub: transparency and collaboration in an open software repository. In Computer Supported Cooperative Work, pages 1277-1286, 2012.Google Scholar
- David Meyer. Amazon Reportedly Killed an AI Recruitment System Because It Couldn't Stop the Tool from Discriminating Against Women. https://https: //fortune.com/ 2018 /10/10/amazon-ai-recruitment-bias-women-sexist/.Google Scholar
- J. Diedrichsen and R. Shadmehr. Detecting and adjusting for artifacts in fMRI time series data. NeuroImage, 27 ( 3 ): 624-634, 2005.Google ScholarCross Ref
- J. J. Dolado, M. C. Otero, and M. Harman. Equivalence hypothesis testing in experimental software engineering. Software Quality Journal, 22 ( 2 ): 215-238, 2014.Google ScholarDigital Library
- J. Duraes, H. Madeira, J. Castelhano, C. Duarte, and M. C. Branco. WAP: Understanding the Brain at Software Debugging. In International Symposium on Software Reliability Engineering, pages 87-92, 2016.Google ScholarCross Ref
- M. Fagan. Design and code inspections to reduce errors in program development. In Software pioneers, pages 575-607. Springer, 2002.Google ScholarCross Ref
- S. Fakhoury, Y. Ma, V. Arnaoudova, and O. Adesope. The efect of poor source code lexicon and readability on developers' cognitive load. In International Conference on Program Comprehension, 2018.Google ScholarDigital Library
- B. Floyd, T. Santander, and W. Weimer. Decoding the representation of code in the brain: An fMRI study of code review and expertise. In International Conference on Software Engineering (ICSE), pages 175-186, 2017.Google ScholarDigital Library
- D. Ford, M. Behroozi, A. Serebrenik, and C. Parnin. Beyond the code itself: how programmers really look at pull requests. In International Conference on Software Engineering: Software Engineering in Society, 2019.Google Scholar
- Z. P. Fry, B. Landau, and W. Weimer. A human study of patch maintainability. In International Symposium on Software Testing and Analysis, pages 177-187, 2012.Google ScholarDigital Library
- G. H. Glover. Overview of functional magnetic resonance imaging. Neurosurgery Clinics, 22 ( 2 ): 133-139, 2011.Google Scholar
- J. H. Goldberg and J. I. Helfman. Comparing information graphics: A critical look at eye tracking. In BEyond Time and Errors: Novel evaLuation Methods for Information Visualization, 2010.Google ScholarDigital Library
- E. H. Gorman and J. A. Kmec. We (have to) try harder: Gender and required work efort in britain and the united states. Gender & Society, 21 ( 6 ): 828-856, 2007.Google ScholarCross Ref
- C. Goues, S. Forrest, and W. Weimer. Current challenges in automatic software repair. Software Quality Journal, 21 ( 3 ): 421-443, Sept. 2013.Google ScholarDigital Library
- M. Gozzi, V. Raymont, J. Solomon, M. Koenigs, and J. Grafman. Dissociable efects of prefrontal and anterior temporal cortical lesions on stereotypical gender attitudes. Neuropsychologia, 47 ( 10 ): 2125-2132, 2009.Google ScholarCross Ref
- A. G. Greenwald, D. E. McGhee, and J. L. Schwartz. Measuring individual diferences in implicit cognition: the implicit association test. Journal of personality and social psychology, 74 ( 6 ): 1464, 1998.Google ScholarCross Ref
- P. Grimm. Social desirability bias. Wiley international encyclopedia of marketing, 2010.Google Scholar
- S. O. Haraldsson, J. R. Woodward, A. E. I. Brownlee, and K. Siggeirsdottir. Fixing Bugs in Your Sleep: How Genetic Improvement Became an Overnight Success. 2017.Google ScholarDigital Library
- J. He, B. S. Butler, and W. R. King. Team cognition: Development and evolution in software project teams. Journal of Management Information Systems, 24 ( 2 ): 261-292, 2007.Google ScholarDigital Library
- M. E. Heilman. Gender stereotypes and workplace bias. Research in organizational Behavior, 32 : 113-135, 2012.Google Scholar
- M. E. Heilman, A. S. Wallen, D. Fuchs, and M. M. Tamkins. Penalties for success: reactions to women who succeed at male gender-typed tasks. Journal of applied psychology, 89 ( 3 ): 416, 2004.Google ScholarCross Ref
- S. Hoogendoorn, H. Oosterbeek, and M. Van Praag. The impact of gender diversity on the performance of business teams: Evidence from a field experiment. Management Science, 59 ( 7 ): 1514-1528, 2013.Google Scholar
- Y. Huang, X. Liu, R. Krueger, T. Santander, X. Hu, K. Leach, and W. Weimer. Distilling neural representations of data structure manipulation using fMRI and fNIRS. In International Conference on Software Engineering (ICSE), 2019.Google ScholarDigital Library
- Y. Ikutani and H. Uwano. Brain activity measurement during program comprehension with nirs. In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pages 1-6. IEEE, 2014.Google Scholar
- N. Imtiaz, J. Middleton, J. Chakraborty, N. Robson, G. Bai, and E. R. Murphy-Hill. Investigating the efects of gender bias on GitHub. In International Conference on Software Engineering (ICSE), pages 700-711, 2019.Google Scholar
- J. D. Ivory. Still a man's game: Gender representation in online reviews of video games. Mass Communication & Society, 9 ( 1 ): 103-114, 2006.Google Scholar
- R. J. Jacob and K. S. Karn. Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. Mind, 2 ( 3 ): 4, 2003.Google Scholar
- X. Jiang, E. Rosen, T. Zefiro, J. VanMeter, V. Blanz, and M. Riesenhuber. Evaluation of a shape-based model of human face discrimination using fmri and behavioral techniques. Neuron, 50 ( 1 ): 159-172, 2006.Google ScholarCross Ref
- D. M. Johnson and D. H. Roen. Complimenting and involvement in peer reviews: Gender variation. Language in society, 21 ( 1 ): 27-57, 1992.Google Scholar
- C. Jones. Measuring defect potentials and defect removal eficiency. CrossTalk The Journal of Defense Software Engineering, 21 ( 6 ): 11-13, 2008.Google Scholar
- M. A. Just and P. A. Carpenter. A theory of reading: from eye fixations to comprehension. Psychological review, 87 ( 4 ): 329, 1980.Google ScholarCross Ref
- N. Kennedy. How google does web-based code reviews with mondrian, 2006.Google Scholar
- D. Kim, J. Nam, J. Song, and S. Kim. Automatic patch generation learned from human-written patches. In 2013 35th International Conference on Software Engineering (ICSE), pages 802-811. IEEE, 2013.Google ScholarDigital Library
- S.-G. Kim and S. Ogawa. Biophysical and physiological origins of blood oxygenation level-dependent fmri signals. Journal of Cerebral Blood Flow & Metabolism, 32 ( 7 ): 1188-1206, 2012.Google ScholarCross Ref
- B. A. Kitchenham, S. L. Pfleeger, L. M. Pickard, P. W. Jones, D. C. Hoaglin, K. E. Emam, and J. Rosenberg. Preliminary guidelines for empirical research in software engineering. IEEE Transactions on Software Engineering, 28 ( 8 ): 721-734, Aug. 2002.Google ScholarDigital Library
- S. Knobloch-Westerwick, C. J. Glynn, and M. Huge. The matilda efect in science communication: an experiment on gender bias in publication quality perceptions and collaboration interest. Science Communication, 35 ( 5 ): 603-625, 2013.Google ScholarCross Ref
- R. Krueger, Y. Huang, X. Liu, T. Santander, W. Weimer, and K. Leach. Neurological divide: An fmri study of prose and code writing. In International Conference on Software Engineering, 2020.Google Scholar
- C. Le Goues, M. Dewey-Vogt, S. Forrest, and W. Weimer. A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each. In International Conference on Software Engineering, 2012.Google Scholar
- F. Long and M. Rinard. Automatic patch generation by learning correct code. In Principles of Programming Languages, 2016.Google Scholar
- Q. Luo, M. Nakic, T. Wheatley, R. Richell, A. Martin, and R. J. R. Blair. The neural basis of implicit moral attitude-an iat study using event-related fmri. Neuroimage, 30 ( 4 ): 1449-1457, 2006.Google Scholar
- J. B. Lyons, N. T. Ho, W. E. Fergueson, G. G. Sadler, S. D. Cals, C. E. Richardson, and M. A. Wilkins. Trust of an automatic ground collision avoidance technology: A fighter pilot perspective. Military Psychology, 28 ( 4 ): 271-277, 2016.Google ScholarCross Ref
- D. S. Ma, J. Correll, and B. Wittenbrink. The chicago face database: A free stimulus set of faces and norming data. Behavior research methods, 47 ( 4 ): 1122-1135, 2015.Google ScholarCross Ref
- D. A. Magezi. Linear mixed-efects models for within-participant psychology experiments: an introductory tutorial and free, graphical user interface (lmmgui). Frontiers in psychology, 6:2, 2015.Google Scholar
- A. Marginean, J. Bader, S. Chandra, M. Harman, Y. Jia, K. Mao, A. Mols, and A. Scott. SapFix: Automated end-to-end repair at scale. In International Conference on Software Engineering: Software Engineering in Practice, 2019.Google Scholar
- J. Marlow, L. Dabbish, and J. Herbsleb. Impression formation in online peer production: activity traces and personal profiles in GitHub. In Computer Supported Cooperative Work, 2013.Google Scholar
- H. W. Marsh, L. Bornmann, R. Mutz, H.-D. Daniel, and A. O'Mara. Gender efects in the peer reviews of grant proposals: A comprehensive meta-analysis comparing traditional and multilevel approaches. Review of Educational Research, 79 ( 3 ): 1290-1326, 2009.Google Scholar
- S. Merritt, L. Shirase, and G. Foster. Normed images for x-ray screening vigilance tasks. Journal of Open Psychology Data, 8 ( 1 ), 2020.Google Scholar
- M. Monperrus. Automatic software repair: A bibliography. ACM Comput. Surv., 51 ( 1 ), Jan. 2018.Google Scholar
- M. Monperrus, S. Urli, T. Durieux, M. Martinez, B. Baudry, and L. Seinturier. Repairnator patches programs automatically. Ubiquity, 2019 (July), July 2019.Google ScholarDigital Library
- D. Nafus. 'patches don't have gender': What is not open in open source software. New Media & Society, 14 ( 4 ): 669-683, 2012.Google Scholar
- T. Nakagawa, Y. Kamei, H. Uwano, A. Monden, K. Matsumoto, and D. M. German. Quantifying programmers' mental workload during program comprehension based on cerebral blood flow measurement: A controlled experiment. In International Conference on Software Engineering, 2014.Google Scholar
- B. A. Nosek, A. G. Greenwald, and M. R. Banaji. Understanding and using the implicit association test: Ii. method variables and construct validity. Personality and Social Psychology Bulletin, 31 ( 2 ): 166-180, 2005.Google Scholar
- U. Obaidellah, M. Al Haek, and P. C.-H. Cheng. A survey on the usage of eyetracking in computer programming. ACM Comput. Surv., 51 ( 1 ):5: 1-5 : 58, Jan. 2018.Google ScholarDigital Library
- N. Peitek, J. Siegmund, C. Parnin, S. Apel, J. Hofmeister, and A. Brechmann. Simultaneous Measurement of Program Comprehension with fMRI and Eye Tracking: A Case Study. In Symposium on Empirical Software Engineering and Measurement, 2018. To appear.Google ScholarDigital Library
- V. Pieterse, D. G. Kourie, and I. P. Sonnekus. Software engineering team diversity and performance. In South African institute of computer scientists and information technologists on IT research in developing countries, 2006.Google ScholarDigital Library
- A. Poole and L. J. Ball. Eye tracking in human-computer interaction and usability research: Current status and future. In Encyclopedia of Human-Computer Interaction, 2005.Google Scholar
- S. Quadflieg, D. J. Turk, G. D. Waiter, J. P. Mitchell, A. C. Jenkins, and C. N. Macrae. Exploring the neural correlates of social stereotyping. Journal of Cognitive Neuroscience, 21 ( 8 ): 1560-1570, 2009.Google ScholarDigital Library
- K. Rayner. Eye movements in reading and information processing. Psychological Bulletin, 85 ( 3 ): 618-660, 1978.Google Scholar
- P. C. Rigby, D. M. German, and M.-A. Storey. Open source software peer review practices: a case study of the apache server. In Proceedings of the 30th international conference on Software engineering, pages 541-550. ACM, 2008.Google ScholarDigital Library
- G. Robles, L. Arjona Reina, A. Serebrenik, B. Vasilescu, and J. M. GonzálezBarahona. Floss 2013 : A survey dataset about free software contributors: challenges for curating, sharing, and combining. In Proceedings of the 11th Working Conference on Mining Software Repositories, pages 396-399. ACM, 2014.Google ScholarDigital Library
- G. Robles, L. A. Reina, J. M. González-Barahona, and S. D. Domínguez. Women in free/libre/open source software: The situation in the 2010s. In IFIP International Conference on Open Source Systems, pages 163-173. Springer, 2016.Google ScholarCross Ref
- P. L. Roth, K. L. Purvis, and P. Bobko. A meta-analysis of gender group diferences for measures of job performance in field studies. Journal of Management, 38 ( 2 ): 719-739, 2012.Google ScholarCross Ref
- T. J. Ryan, G. M. Alarcon, C. Walter, R. Gamble, S. A. Jessup, A. Capiola, and M. D. Pfahler. Trust in automated software repair. In International Conference on Human-Computer Interaction, pages 452-470. Springer, 2019.Google ScholarCross Ref
- S. Sarkar and C. Parnin. Characterizing and predicting mental fatigue during programming tasks. In Emotion Awareness in Software Engineering, 2017.Google ScholarCross Ref
- J. R. Shapiro and S. L. Neuberg. From stereotype threat to stereotype threats: Implications of a multi-threat framework for causes, moderators, mediators, consequences, and interventions. Personality and Social Psychology Review, 11 ( 2 ): 107-130, 2007.Google ScholarCross Ref
- Z. Sharafi, T. Shafer, B. Sharif, and Y.-G. Guéhéneuc. Eye-tracking metrics in software engineering. In 2015 Asia-Pacific Software Engineering Conference (APSEC), pages 96-103. IEEE, 2015.Google ScholarCross Ref
- Z. Sharafi, Z. Soh, and Y.-G. Guéhéneuc. A systematic literature review on the usage of eye-tracking in software engineering. Inf. Softw. Technol., 67(C): 79-107, Nov. 2015.Google Scholar
- Z. Sharafi, Z. Soh, Y.-G. Guéhéneuc, and G. Antoniol. Women and men-diferent but equal: On the impact of identifier style on source code reading. In International Conference on Program Comprehension, 2012.Google Scholar
- B. Sharif, M. Falcone, and J. I. Maletic. An eye-tracking study on the role of scan time in finding source code defects. In Symposium on Eye Tracking Research and Applications, 2012.Google ScholarDigital Library
- J. Siegmund, C. Kästner, S. Apel, C. Parnin, A. Bethmann, T. Leich, G. Saake, and A. Brechmann. Understanding understanding source code with functional magnetic resonance imaging. In International Conference on Software Engineering, pages 378-389, 2014.Google Scholar
- J. Siegmund, N. Peitek, C. Parnin, S. Apel, J. Hofmeister, C. Kästner, A. Begel, A. Bethmann, and A. Brechmann. Measuring Neural Eficiency of Program Comprehension. In Foundations of Software Engineering, pages 140-150, 2017.Google Scholar
- N. Subrahmaniyan, L. Beckwith, V. Grigoreanu, M. Burnett, S. Wiedenbeck, V. Narayanan, K. Bucht, R. Drummond, and X. Fern. Testing vs. code inspection vs. what else?: Male and female end users' debugging strategies. In Human Factors in Computing Systems, 2008.Google ScholarDigital Library
- J. Terrell, A. Kofink, J. Middleton, C. Rainear, E. Murphy-Hill, C. Parnin, and J. Stallings. Gender diferences and bias in open source: Pull request acceptance of women versus men. PeerJ Computer Science, 3 : e111, 2017.Google ScholarCross Ref
- J. Tsay, L. Dabbish, and J. Herbsleb. Influence of social and technical factors for evaluating contribution in github. In Proceedings of the 36th international conference on Software engineering, pages 356-366. ACM, 2014.Google ScholarDigital Library
- P. Tse and K. Hyland. 'robot kung fu': Gender and professional identity in biology and philosophy reviews. Journal of Pragmatics, 40 ( 7 ): 1232-1248, 2008.Google ScholarCross Ref
- A. Tsotsis. Meet phabricator, the witty code review tool built inside facebook. City, 2006.Google Scholar
- S. Urli, Z. Yu, L. Seinturier, and M. Monperrus. How to design a program repair bot? insights from the Repairnator project. In International Conference on Software Engineering: Software Engineering in Practice, 2018.Google ScholarDigital Library
- H. Uwano, M. Nakamura, A. Monden, and K.-i. Matsumoto. Analyzing individual performance of source code review using reviewers' eye movement. In Eye Tracking Research Applications, 2006.Google ScholarDigital Library
- R. Van Megen and D. B. Meyerhof. Costs and benefits of early defect detection: experiences from developing client server and host applications. Software Quality Journal, 4 ( 4 ): 247-256, 1995.Google ScholarCross Ref
- R. van Tonder and C. Le Goues. Towards s/engineer/bot: Principles for program repair bots. In 2019 IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE), pages 43-47, May 2019.Google ScholarDigital Library
- B. Vasilescu, D. Posnett, B. Ray, M. G. van den Brand, A. Serebrenik, P. Devanbu, and V. Filkov. Gender and tenure diversity in github teams. In Human factors in computing systems, 2015.Google ScholarDigital Library
- M. Vorvoreanu, L. Zhang, Y.-H. Huang, C. Hilderbrand, Z. Steine-Hanson, and M. Burnett. From gender biases to gender-inclusive design: An empirical investigation. In Human Factors in Computing Systems, 2019.Google ScholarDigital Library
- D. Wakabayashi. Google finds it's underpaying many men as it addresses wage equity. https://www.nytimes.com/ 2019 /03/04/technology/google-gender-paygap.html, March 2019.Google Scholar
- M. Welsh. My love afair with code reviews. http://matt-welsh.blogspot.com/ 2012 /02/my-love-afair-with-code-reviews.html, 2012. [Online; accessed 4-September-2019].Google Scholar
- J. O. Wobbrock, L. Findlater, D. Gergle, and J. J. Higgins. The aligned rank transform for nonparametric factorial analyses using only ANOVA procedures. In Human factors in computing systems, 2011.Google ScholarDigital Library
- yeeguy. How Facebook Ships Code. https://framethink.wordpress.com/ 2011 / 01/17/how-facebook-ships-code/, 2011. [Online; accessed 4-September-2019].Google Scholar
- S. Zweben and B. Bizot. 2017 CRA Taulbee Survey. Computing Research News, 30 ( 5 ): 1-47, 2018.Google Scholar
Index Terms
- Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines
Recommendations
Trustworthiness Perceptions in Code Review: An Eye-tracking Study
ESEM '20: Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)Background: Automated program repair and other bug-fixing approaches are gaining attention in the software engineering community. Automation shows promise in reducing bug fixing costs. However, many developers express reluctance about accepting machine-...
Using Eye-Tracking to Unveil Differences Between Kids and Teens in Coding Activities
IDC '17: Proceedings of the 2017 Conference on Interaction Design and ChildrenComputational thinking and coding is gradually becoming an important part of K-12 education. Most parents, policy makers, teachers, and industrial stakeholders want their children to attain computational thinking and coding competences, since learning ...
Eye movements in code review
EMIP '18: Proceedings of the Workshop on Eye Movements in ProgrammingIn order to ensure sufficient quality, software engineers conduct code reviews to read over one another's code looking for errors that should be fixed before committing to their source code repositories. Many kinds of errors are spotted, from simple ...
Comments