research-article

Developers' code context models for change tasks

Authors:

David C. Shepherd,

Christoph BräunlichAuthors Info & Claims

FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

Pages 7 - 18

https://doi.org/10.1145/2635868.2635905

Published: 11 November 2014 Publication History

Abstract

To complete a change task, software developers spend a substantial amount of time navigating code to understand the relevant parts. During this investigation phase, they implicitly build context models of the elements and relations that are relevant to the task. Through an exploratory study with twelve developers completing change tasks in three open source systems, we identified important characteristics of these context models and how they are created. In a second empirical analysis, we further examined our findings on data collected from eighty developers working on a variety of change tasks on open and closed source projects. Our studies uncovered, amongst other results, that code context models are highly connected, structurally and lexically, that developers start tasks using a combination of search and navigation and that code navigation varies substantially across developers. Based on these findings we identify and discuss design requirements to better support developers in the initial creation of code context models. We believe this work represents a substantial step in better understanding developers' code navigation and providing better tool support that will reduce time and effort needed for change tasks.

References

[1]

bugs.eclipse.org/bugs/.

[2]

https://github.com/abb-iss/study-artifacts-for-codecontext-models.

[3]

www.eclipse.org/mylyn/.

[4]

V. R. Basili, R. W. Selby, and D. H. Hutchens. Experimentation in software engineering. IEEE Trans. on Softw. Eng., 1986.

Digital Library

[5]

A. Bragdon, R. Zeleznik, S. P. Reiss, S. Karumuri, W. Cheung, J. Kaplan, C. Coleman, F. Adeputra, and J. J. LaViola, Jr. Code bubbles: a working set-based interface for code understanding and maintenance. In Proc. of CHI’10, 2010.

Digital Library

[6]

R. Brooks. Using a behavioral theory of program comprehension in software engineering. In Proceedings of the 3rd international conference on Software engineering, ICSE ’78, pages 196–201, Piscataway, NJ, USA, 1978. IEEE Press.

Digital Library

[7]

R. Brooks. Towards a theory of the comprehension of computer programs. International Journal of Man-Machine Studies, 18(6):543 – 554, 1983.

[8]

C. L. Corritore and S. Wiedenbeck. Mental representations of expert procedural and object-oriented programmers in a software maintenance task. Int. Journal of Human-Computer Studies, 50(1):61 – 83, 1999.

Digital Library

[9]

R. DeLine, A. Khella, M. Czerwinski, and G. Robertson. Towards understanding programs through wearbased filtering. In Proc. of SoftVis’05, pages 183–192. ACM, 2005.

Digital Library

[10]

B. Fluri, M. Wursch, M. Pinzger, and H. Gall. Change distilling:tree differencing for fine-grained source code change extraction. Software Engineering, IEEE Transactions on, 33(11):725–743, 2007.

Digital Library

[11]

E. Hill, L. Pollock, and K. Vijay-Shanker. Exploring the neighborhood with dora to expedite software maintenance. In Proc. of ASE’07, 2007.

Digital Library

[12]

D. Janzen and K. De Volder. Navigating and querying code without getting lost. In Proceedings of the 2nd international conference on Aspect-oriented software development, pages 178–187. ACM, 2003.

Digital Library

[13]

M. Kersten and G. C. Murphy. Using task context to improve programmer productivity. In Proc. of FSE’06, 2006.

Digital Library

[14]

A. J. Ko, H. H. Aung, and B. A. Myers. Eliciting design requirements for maintenance-oriented ides: a detailed study of corrective and perfective maintenance tasks. In Software Engineering, 2005. ICSE 2005. Proceedings. 27th International Conference on, pages 126–135. IEEE, 2005.

Digital Library

[15]

A. J. Ko, B. A. Myers, M. J. Coblenz, and H. H. Aung. An exploratory study of how developers seek, relate, and collect relevant information during soft. maintenance tasks. IEEE Trans. on Soft. Eng., 32:971–987, 2006.

Digital Library

[16]

T. D. LaToza, D. Garlan, J. D. Herbsleb, and B. A. Myers. Program comprehension as fact finding. In Proc. of ESEC/FSE’07, 2007.

Digital Library

[17]

T. D. LaToza, G. Venolia, and R. DeLine. Maintaining mental models: a study of developer work habits. In Proc. of ICSE’06, 2006.

Digital Library

[18]

J. Lawrance, R. Bellamy, M. Burnett, and K. Rector. Using information scent to model the dynamic foraging behavior of programmers in maintenance tasks. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008.

Digital Library

[19]

C. McMillan, M. Grechanik, D. Poshyvanyk, Q. Xie, and C. Fu. Portfolio: finding relevant functions and their usage. In Proc. of ICSE’11.

Digital Library

[20]

G. C. Murphy, M. Kersten, M. P. Robillard, and D. ˇ Cubrani´ c. The emergent structure of development tasks. In Proc. of ECOOP’05, pages 33–48, 2005.

Digital Library

[21]

N. Pennington. Stimulus structures and mental representations in expert comprehension of computer programs. Cognitive Psychology, 19(3):295 – 341, 1987.

[22]

D. Piorkowski, S. Fleming, C. Scaffidi, C. Bogart, M. Burnett, B. John, R. Bellamy, and C. Swart. Reactive information foraging: an empirical investigation of theory-based recommender systems for programmers. In Proc. of Human Factors in Computing Sys., 2012.

Digital Library

[23]

M. Robillard, W. Coelho, and G. Murphy. How effective developers investigate source code: an exploratory study. IEEE Trans. on Softw. Eng., 30(12):889 – 903, dec. 2004.

Digital Library

[24]

M. P. Robillard and G. C. Murphy. Concern graphs: finding and describing concerns using structural program dependencies. In Proc. of ICSE’02, 2002.

Digital Library

[25]

C. Seaman. Qualitative methods in empirical studies of software engineering. IEEE Trans. on Soft. Eng., 25(4):557 –572, jul/aug 1999.

Digital Library

[26]

J. Sillito, K. De Voider, B. Fisher, and G. Murphy. Managing software change tasks: An exploratory study. In Empirical Software Engineering, 2005. 2005 International Symposium on, pages 10–pp. IEEE, 2005.

[27]

J. Sillito, G. C. Murphy, and K. D. Volder. Questions programmers ask during software evolution tasks. In Proc. of FSE’06, 2006.

Digital Library

[28]

J. Singer, T. Lethbridge, N. Vinson, and N. Anquetil. An examination of software engineering work practices. In Proc. of Centre for Adv. Studies on Collaborative Research, 1997.

Digital Library

[29]

W. Snipes, A. Nair, and E. Murphy-Hill. Experiences Gamifying Developer Adoption of Practices and Tools. In Software Engineering, 2014. ICSE 2014. IEEE 36st International Conference on, 2014.

Digital Library

[30]

J. Starke, C. Luce, and J. Sillito. Searching and skimming: An exploratory study. In Proc. of ICSM’09, 2009.

[31]

M.-A. Storey, F. D. Fracchia, and H. A. Müller. Cognitive design elements to support the construction of a mental model during software exploration. Journal of Systems and Software, 44(3):171–185, 1999.

Digital Library

[32]

A. von Mayrhauser and A. M. Vans. Comprehension processes during large scale maintenance. In Proc. of ICSE’94., 1994.

Digital Library

[33]

J. Wang, X. Peng, Z. Xing, and W. Zhao. An exploratory study of feature location process: Distinct phases, recurring patterns, and elementary actions. 2011.

Cited By

Razzaq ABuckley JLai QYu TBotterweck G(2024)A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers' Productivity: Factors, Practices, and RecommendationsACM Computing Surveys10.1145/368729957:1(1-46)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3687299
Etaiwi LSager PGuéhéneuc YHamel S(2024)Consensus task interaction trace recommender to guide developers’ software navigationEmpirical Software Engineering10.1007/s10664-024-10528-729:6Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1007/s10664-024-10528-7
Wang YLin YWan ZYang XGrundy J(2023)Task Context: A Tool for Predicting Code Context Models for Software Development TasksProceedings of the 45th International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion58688.2023.00045(156-160)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE-Companion58688.2023.00045
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Index Terms

Developers' code context models for change tasks
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments

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Published In

cover image ACM Conferences

FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

November 2014

856 pages

ISBN:9781450330565

DOI:10.1145/2635868

General Chair:
Shing-Chi Cheung
Hong Kong University of Science and Technology, China
,
Program Chairs:
Alessandro Orso
Georgia Institute of Technology, USA
,
Margaret-Anne Storey
University of Victoria, Canada

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSOFT: ACM Special Interest Group on Software Engineering

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Publication History

Published: 11 November 2014

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SIGSOFT/FSE'14

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SIGSOFT

SIGSOFT/FSE'14: 22nd ACM SIGSOFT Symposium on the Foundations of Software Engineering

November 16 - 21, 2014

Hong Kong, China

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Overall Acceptance Rate 17 of 128 submissions, 13%

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Cited By

Razzaq ABuckley JLai QYu TBotterweck G(2024)A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers' Productivity: Factors, Practices, and RecommendationsACM Computing Surveys10.1145/368729957:1(1-46)Online publication date: 7-Oct-2024
https://dl.acm.org/doi/10.1145/3687299
Etaiwi LSager PGuéhéneuc YHamel S(2024)Consensus task interaction trace recommender to guide developers’ software navigationEmpirical Software Engineering10.1007/s10664-024-10528-729:6Online publication date: 2-Sep-2024
https://dl.acm.org/doi/10.1007/s10664-024-10528-7
Wang YLin YWan ZYang XGrundy J(2023)Task Context: A Tool for Predicting Code Context Models for Software Development TasksProceedings of the 45th International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion58688.2023.00045(156-160)Online publication date: 14-May-2023
https://dl.acm.org/doi/10.1109/ICSE-Companion58688.2023.00045
Simhandl GPaulweber PZdun U(2023)Developer's Cognitive Effort Maintaining Monoliths vs. Microservices - An Eye-Tracking Study2023 30th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC60848.2023.00044(339-348)Online publication date: 4-Dec-2023
https://doi.org/10.1109/APSEC60848.2023.00044
Heinonen ALehtelä BHellas AFagerholm F(2023)Synthesizing research on programmers’ mental models of programs, tasks and concepts — A systematic literature reviewInformation and Software Technology10.1016/j.infsof.2023.107300164(107300)Online publication date: Dec-2023
https://doi.org/10.1016/j.infsof.2023.107300
Nagaria BHall T(2022)How Software Developers Mitigate Their Errors When Developing CodeIEEE Transactions on Software Engineering10.1109/TSE.2020.304055448:6(1853-1867)Online publication date: 1-Jun-2022
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Sharafi ZBertram IFlanagan MWeimer W(2022)Eyes on Code: A Study on Developers’ Code Navigation StrategiesIEEE Transactions on Software Engineering10.1109/TSE.2020.303206448:5(1692-1704)Online publication date: 1-May-2022
https://doi.org/10.1109/TSE.2020.3032064
Mondal SDas PBhattacharjee Rudra T(2022)Measuring code comprehension effort using code reading patternSādhanā10.1007/s12046-022-01876-547:3Online publication date: 17-Jun-2022
https://doi.org/10.1007/s12046-022-01876-5
Horváth FBeszédes ÁVancsics BBalogh GVidács LGyimóthy T(2022)Using contextual knowledge in interactive fault localizationEmpirical Software Engineering10.1007/s10664-022-10190-x27:6Online publication date: 1-Nov-2022
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Wan ZMurphy GXia XGrundy JLe Goues CLo D(2020)Predicting code context models for software development tasksProceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering10.1145/3324884.3416544(809-820)Online publication date: 21-Dec-2020
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