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Measuring program comprehension: a large-scale field study with professionals

Published: 27 May 2018 Publication History

Abstract

During software development and maintenance, developers spend a considerable amount of time on program comprehension. Previous studies show that program comprehension takes up as much as half of a developer's time. However, most of these studies are performed in a controlled setting, or with a small number of participants, and investigate the program comprehension activities only within the IDEs. However, developers' program comprehension activities go well beyond their IDE interactions.
In this paper [1], we perform a more realistic investigation of program comprehension activities. To do this, we extend our ActivitySpace framework to collect and analyze Human-Computer Interaction (HCI) data across many applications (not just the IDEs). We collect 3,148 working hour data from 78 professional developers in a field study. We follow Minelli et al.'s approach to assign developers' activities into four categories: navigation, editing, comprehension, and other. Then we measure comprehension time by calculating the time that developers spend on program comprehension. We find that on average developers spend ~ 58% of their time on program comprehension activities, and that they frequently use web browsers and document editors to perform program comprehension activities. We also investigate the impact of programming language, developers' experience, and project phase on the time that is spent on program comprehension.

Reference

[1]
Xin Xia, Lingfeng Bao, David Lo, Zhenchang Xing, Ahmed E. Hassan, and Shanping Li. 2017. Measuring Program Comprehension: A Large-Scale Field Study with Professionals. IEEE Transactions on Software Engineering PP, 99 (2017), 1--1.

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Published In

cover image ACM Conferences
ICSE '18: Proceedings of the 40th International Conference on Software Engineering
May 2018
1307 pages
ISBN:9781450356381
DOI:10.1145/3180155
  • Conference Chair:
  • Michel Chaudron,
  • General Chair:
  • Ivica Crnkovic,
  • Program Chairs:
  • Marsha Chechik,
  • Mark Harman
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

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Author Tags

  1. field study
  2. inference model
  3. program comprehension

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ICSE '18
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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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  • (2023)Summarizing source code with Heterogeneous Syntax Graph and dual positionInformation Processing and Management: an International Journal10.1016/j.ipm.2023.10341560:5Online publication date: 1-Sep-2023
  • (2022)Evaluating Atoms of Confusion in the Context of Code Reviews2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME55016.2022.00048(404-408)Online publication date: Oct-2022
  • (2022)Automatic source code summarization with graph attention networksJournal of Systems and Software10.1016/j.jss.2022.111257188:COnline publication date: 1-Jun-2022
  • (2021)Improving Code Summarization Through Automated Quality Assurance2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE52982.2021.00057(486-497)Online publication date: Oct-2021
  • (2021)The Mind Is a Powerful PlaceProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00055(512-523)Online publication date: 22-May-2021
  • (2021)NeuralDoc-Automating Code Translation Using Machine LearningArtificial Intelligence Driven Circuits and Systems10.1007/978-981-16-6940-8_11(125-138)Online publication date: 15-Dec-2021
  • (2020)Deep code comment generation with hybrid lexical and syntactical informationEmpirical Software Engineering10.1007/s10664-019-09730-925:3(2179-2217)Online publication date: 1-May-2020
  • (2019)Automatic Source Code Summarization with Extended Tree-LSTM2019 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2019.8851751(1-8)Online publication date: Jul-2019
  • (2019)Towards a Model to Appraise and Suggest Identifier Names2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2019.00103(639-643)Online publication date: Sep-2019
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