Understanding the Impact of Pair Programming on the Minds of Developers

Software is mostly, if not entirely, a knowledge artifact. Software best practices are often thought to work because they induce more productive behaviour in software developers. In this paper we deployed a new generation tool, portable multichannel EEG, to obtain direct physical insight into the mental processes of working software developers engaged in their standard activities. We have demonstrated the feasibility of this approach and obtained a glimpse of its potential power to distinguish physical brain activity of developers working with different methodologies.

[1]  Alberto Sillitti,et al.  An interpretation of the results of the analysis of pair programming during novices integration in a team , 2009, 2009 3rd International Symposium on Empirical Software Engineering and Measurement.

[2]  Thomas Fritz,et al.  Interruptibility of Software Developers and its Prediction Using Psycho-Physiological Sensors , 2015, CHI.

[3]  Rytis Maskeliunas,et al.  Distributed under Creative Commons Cc-by 4.0 Consumer-grade Eeg Devices: Are They Usable for Control Tasks? , 2022 .

[4]  Witold Pedrycz,et al.  Preliminary Analysis of the Effects of Pair Programming on Job Satisfaction , 2001 .

[5]  Alberto Sillitti,et al.  Pair Programming and Software Defects--A Large, Industrial Case Study , 2013, IEEE Transactions on Software Engineering.

[6]  Michelle N. Lumicao,et al.  EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. , 2007, Aviation, space, and environmental medicine.

[7]  Soraia M. Alarcão,et al.  Emotions Recognition Using EEG Signals: A Survey , 2019, IEEE Transactions on Affective Computing.

[8]  Thomas Fritz,et al.  Leveraging Biometric Data to Boost Software Developer Productivity , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).

[9]  Thomas Leich,et al.  Understanding understanding source code with functional magnetic resonance imaging , 2014, ICSE.

[10]  Westley Weimer,et al.  Decoding the Representation of Code in the Brain: An fMRI Study of Code Review and Expertise , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE).

[11]  Begoña Garcia-Zapirain,et al.  EEG artifact removal—state-of-the-art and guidelines , 2015, Journal of neural engineering.

[12]  Sven Apel,et al.  Measuring neural efficiency of program comprehension , 2017, ESEC/SIGSOFT FSE.

[13]  T. Demiralp,et al.  Comparative analysis of event-related potentials during Go/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention , 2006, Brain Research.

[14]  Thomas Fritz,et al.  Stuck and Frustrated or in Flow and Happy: Sensing Developers' Emotions and Progress , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[15]  D. L. Schomer,et al.  Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields , 2012 .

[16]  Danial Hooshyar,et al.  Comparing Programming Language Comprehension between Novice and Expert Programmers Using EEG Analysis , 2016, 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE).

[17]  Andrew Begel,et al.  Using psycho-physiological measures to assess task difficulty in software development , 2014, ICSE.

[18]  Giancarlo Succi,et al.  What do software engineers care about? gaps between research and practice , 2017, ESEC/SIGSOFT FSE.

[19]  Alberto Sillitti,et al.  Understanding the impact of Pair Programming on developers attention: A case study on a large industrial experimentation , 2012, 2012 34th International Conference on Software Engineering (ICSE).