What Developers (Care to) Recall: An Interview Survey on Smaller Systems

Krüger, Jacob; Hebig, Regina

doi:10.1109/icsme46990.2020.00015

Cited by 7 publications

(3 citation statements)

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“…The findings in reply to RQ2 are complementary to a related study [11] that concluded from developer interviews that abstract knowledge of the source code is more important to remember. However, our results are somewhat contradictory of another study by Krüger et al [12] who found that the forgetting curve applies to remembering source code.…”

Section: Resultssupporting

confidence: 53%

“…Krüger et al examined feature traceability and program decomposition and found that feature traces were helpful in solving tasks more quickly, while program decomposition hindered it [10]. They also found based on developer interviews that self-assessments are reliable sources of developer-related information, and programmers tend to be correct when they recall memories on project-related questions that they consider important [11]. Their findings confirm the study of Koenemann and Robertson who investigated the analysis methods of professional developers, and found that they focus on the software parts that they perceive as relevant to them [9].…”

Section: Program Comprehension Experimentsmentioning

confidence: 99%

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Field Experiment of the Memory Retention of Programmers Regarding Source Code

Fekete,

Porkoláb

2023

Studia UBB Informatica

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"Program comprehension is a continuously important topic in computer science since the spread of personal computers, and several program comprehension models have been identified as possible directions of active code comprehension. There has been little research on how much programmers remember the code they have once written. We conducted two experiments with a group of Computer Science MSc students. In the first experiment, we examined the code comprehension strategies of the participants. The students were given a task to implement a minor feature in a relatively small C++ project. In the second experiment, we asked the students 2 months later to complete the same task again. Before starting the clock, we asked the students to fill a questionnaire which aimed to measure program code-related memory retention: we inquired about how much the students remembered the code, down to the smallest relevant details, e.g. the name of functions and variables they had to find to complete the task. After the second experiment, we could compare the solution times of those students who participated in both parts. As one result, we could see that these students could solve the task in shorter time than they did in the first experiment. We also looked at the results of the questionnaire: the vast majority of students could not precisely remember more than two or three identifiers from the original code. In this paper, we will show how this result compares to the forgetting curve. 2010 Mathematics Subject Classification. 68U99. 1998 CR Categories and Descriptors. I.m [Computing Methodologies]: Miscellaneous; J.m [Computer Applications]: Miscellaneous. Key words and phrases. code comprehension, memory retention, experiment."

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Section: Resultssupporting

confidence: 53%

Section: Program Comprehension Experimentsmentioning

confidence: 99%

Field Experiment of the Memory Retention of Programmers Regarding Source Code

Fekete,

Porkoláb

2023

Studia UBB Informatica

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“…There have been a number of early exploratory user studies [10,13,18], and recent follow-up works [2,11,12,19] that categorize program comprehension questions and engineers' information needs. We will perform a literature review of this field to identify questions of interest to engineers that can be applied to graphical models of program data, especially those supporting program comprehension and interaction detection.…”

Section: Related Workmentioning

confidence: 99%

Interactive Graph Exploration for Comprehension of Static Analysis Results

Toledo

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Static analysis results can be overwhelming depending on their complexity and the total number of results. Interactive graph visualization can help engineers explore the connections between different code entities while visually supporting insights about the code's behaviour. In our doctoral research, we aim to investigate how a graphical model of a program and its analysis results can support the engineer's understanding. We expect that a graphical interface can ease the diagnose of faults and reduce the cognitive load required to comprehend reported control and data flows present in the codebase.

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