Measuring neural efficiency of program comprehension

Siegmund, Janet; Peitek, Norman; Parnin, Chris; Apel, Sven; Hofmeister, Johannes C.; Kästner, Christian; Begel, Andrew; Bethmann, Anja; Brechmann, André

doi:10.1145/3106237.3106268

Cited by 92 publications

(81 citation statements)

References 37 publications

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“…Other novel findings in the present study were potential involvement of the left IT, right MFG, and right IFG Tri with programming expertise. Importantly, these regions were not specified by previous studies focusing on the relationship between brain activity and program comprehension processes [17][18][19][20] , suggesting that the regions might be more related to programming expertise than program comprehension processes. Because the left IT is well known for the function in high-level visual processing including word recognition and categorical object representations [45][46][47] , our results may suggest that high-level visual cortex in expert programmers' brain could be fine-tuned by their training experience to realize faster program comprehension process.…”

Section: Discussionmentioning

confidence: 69%

“…Despite the difference in research aims, a subset of brain regions specified in this study was similar to those specified by prior fMRI studies on programmers [17][18][19] . In particular, this study associated the left IFG, MTG, IPL, SMG with programming expertise while previous studies related them with program comprehension processes.…”

Section: Discussionmentioning

confidence: 91%

“…We then searched in the open codeset for Java code snippets implementing one of the selected algorithms and found 1251 candidates. The reasons why we focused on Java in this study were because the language has been one of the most famous programming languages 67 and prior fMRI studies on programmers also used Java code snippets as experimental stimuli [17][18][19] . To meet the screen size constraint in the MRI scanner, we excluded code snippets with lines of code (LOC) more than 30 and characters per line (CPL) more than 120.…”

Section: Source Code Selection and Normalizationmentioning

confidence: 99%

“…Recent studies have investigated the brain activity of programmers using functional magnetic resonance imaging (fMRI) to examine their cognitive mechanisms. Siegmund et al contrasted brain activity during program output estimations against syntax error searches and showed that the processes of program output estimations activated left-lateralized brain regions in the middle temporal gyrus, inferior parietal lobule, middle and inferior frontal gyri 17,18 . Several studies have also tried to investigate neural correlates of subject-wise programming expertise but failed to find a systematic trend.…”

Section: Introductionmentioning

confidence: 99%

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Expert programmers have fine-tuned cortical representations of source code

Ikutani

Kubo

Nishida

et al. 2020

Preprint

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Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

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Section: Discussionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 91%

Section: Source Code Selection and Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Expert programmers have fine-tuned cortical representations of source code

Ikutani

Kubo

Nishida

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…If code comprehension relies on the same circuits that map form to meaning in natural language, we expect to see activity within the language system. Evidence from prior neuroimaging investigations of code comprehension is inconclusive: some studies have reported left-lateralized activity in the regions that roughly correspond to the language system (Siegmund et al, 2014(Siegmund et al, , 2017, whereas others have observed activity in frontal and parietal regions resembling the MD system (Floyd et al, 2017;Huang et al, 2019). However, none of these prior studies sought to explicitly distinguish code comprehension from other programming-related processes, and most of them did not report whether the code-responsive brain regions they isolated were active during nonprogramming tasks (such as working memory, math, or language; cf.…”

Section: Introductionmentioning

confidence: 99%

Comprehension of computer code relies primarily on domain-general executive brain regions

Ivanova

Srikant

Sueoka

et al. 2020

Preprint

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Computer programming is a novel cognitive tool that has transformed modern society. An integral part of programming is code comprehension: the ability to process individual program tokens, combine them into statements, which, in turn, combine to form a program. What cognitive and neural mechanisms support this ability to process computer code? Here, we used fMRI to investigate the role of two candidate brain systems in code comprehension: the multiple demand (MD) system, typically recruited for math, logic, problem solving, and executive function, and the language system, typically recruited for linguistic processing. Across two experiments, we examined brain responses to code written in two programing languages: Python, a text-based programming language (Experiment 1) and ScratchJr, a graphical programming language for children (Experiment 2). To isolate neural activity evoked by code comprehension per se rather than by processing program content, we contrasted responses to code problems with responses to contentmatched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments. In contrast, the language system responded strongly to sentence problems, but only weakly or not at all to code problems. We conclude that code comprehension relies primarily on domaingeneral executive resources, demonstrating that the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.

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Biometric Measurement in Software Engineering

Fagerholm

Fritz

2020

Contemporary Empirical Methods in Software Engineering

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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 book 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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Measuring neural efficiency of program comprehension

Cited by 92 publications

References 37 publications

Expert programmers have fine-tuned cortical representations of source code

Expert programmers have fine-tuned cortical representations of source code

Comprehension of computer code relies primarily on domain-general executive brain regions

Biometric Measurement in Software Engineering

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