The effect of poor source code lexicon and readability on developers' cognitive load

Fakhoury, Sarah; Ma, Yuzhan; Arnaoudova, Venera; Adesope, Olusola

doi:10.1145/3196321.3196347

Cited by 87 publications

(37 citation statements)

References 31 publications

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“…Our findings suggest that earlier reports of left-lateralized code-evoked activity ( Siegmund et al, 2014 ) may reflect processing program content rather than code comprehension per se. This distinction should also be considered when interpreting results of other studies of programming effects on brain activity, such as debugging ( Castelhano et al, 2019 ), variable tracking ( Ikutani and Uwano, 2014 ; Nakagawa et al, 2014 ), use of semantic cues or program layout ( Fakhoury et al, 2018 ; Siegmund et al, 2017 ), program generation ( Krueger et al, 2020 ), and programming expertise ( Ikutani et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

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

Ivanova

Srikant

Sueoka

et al. 2020

eLife

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Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used functional magnetic resonance imaging to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.

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Section: Discussionmentioning

confidence: 99%

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

Ivanova

Srikant

Sueoka

et al. 2020

eLife

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show abstract

“…Our findings suggest that earlier reports of left-lateralized code-evoked activity (Siegmund et al, 2014) may reflect processing program content rather than code comprehension per se. This distinction should also be considered when interpreting results of other studies of programming effects on brain activity, such as debugging (Castelhano et al, 2019), variable tracking (Ikutani & Uwano, 2014;Nakagawa et al, 2014), use of semantic cues or program layout (Fakhoury et al, 2018;Siegmund et al, 2017), program generation (Krueger et al, 2020), and programming expertise (Ikutani et al, 2020).…”

Section: Discussionmentioning

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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“…Although we do not observe an increase in cognitive load over the treatment snippet for those participants, we do observe and increase in the remaining 40% of participants who do complete the tasks successfully. More details on the empirical study and results may be found in our paper [8].…”

Section: Current Resultsmentioning

confidence: 99%

Moving towards objective measures of program comprehension

Fakhoury

2018

Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Traditionally, program comprehension research relies heavily on indirect measures of comprehension, where subjects report on their own comprehension levels or summarize part of an artifact so that researchers can instead deduce the level of comprehension. However, there are several potential issues that can result from using these indirect measures because they are prone to participant biases and implicitly deduce comprehension based on various factors. The proposed research presents a framework to move towards more objective measures of program comprehension through the use of brain imaging and eye tracking technology. We aim to shed light on how the human brain processes comprehension tasks, specifically what aspects of the source code cause measurable increases in the cognitive load of developers in both bug localization tasks, as well as code reviews. We discuss the proposed methodology, preliminary results, and overall contributions of the work.

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The effect of poor source code lexicon and readability on developers' cognitive load

Cited by 87 publications

References 31 publications

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

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

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

Moving towards objective measures of program comprehension

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