Consensus task interaction trace recommender to guide developers’ software navigation
Developers must complete change tasks on large software systems for maintenance and development purposes. Having a custom software system with numerous instances that meet the growing client demand for features and functionalities increases the software complexity. Developers, especially newcomers,...
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| Published in | Empirical software engineering : an international journal Vol. 29; no. 6; p. 147 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.11.2024
Springer Nature B.V |
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| Abstract | Developers must complete change tasks on large software systems for maintenance and development purposes. Having a custom software system with numerous instances that meet the growing client demand for features and functionalities increases the software complexity. Developers, especially newcomers, must spend a significant amount of time navigating through the source code and switching back and forth between files in order to understand such a system and find the parts relevant for performing current tasks. This navigation can be difficult, time-consuming and affect developers’ productivity. To help guide developers’ navigation towards successfully resolving tasks with minimal time and effort, we present a task-based recommendation approach that exploits aggregated developers’ interaction traces. Our novel approach, Consensus Task Interaction Trace Recommender (CITR), recommends file(s)-to-edit that help perform a set of tasks based on a tasks-related set of interaction traces obtained from developers who performed similar change tasks on the same or different custom instances of the same system. Our approach uses a consensus algorithm, which takes as input task-related interaction traces and recommends a consensus task interaction trace that developers can use to complete given similar change tasks that require editing (a) common file(s). To evaluate the efficiency of our approach, we perform three different evaluations. The first evaluation measures the accuracy of CITR recommendations. In the second evaluation, we assess to what extent CITR can help developers by conducting an observational controlled experiment in which two groups of developers performed evaluation tasks with and without the recommendations of CITR. In the third and last evaluation, we compare CITR to a state-of-the-art recommendation approach, MI. Results report with statistical significance that CITR can correctly recommend on average 73% of the files to be edited. Furthermore, they show that CITR can increase developers’ successful task completion rate. CITR outperforms MI by an average of 31% higher recommendation accuracy. |
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| AbstractList | Developers must complete change tasks on large software systems for maintenance and development purposes. Having a custom software system with numerous instances that meet the growing client demand for features and functionalities increases the software complexity. Developers, especially newcomers, must spend a significant amount of time navigating through the source code and switching back and forth between files in order to understand such a system and find the parts relevant for performing current tasks. This navigation can be difficult, time-consuming and affect developers’ productivity. To help guide developers’ navigation towards successfully resolving tasks with minimal time and effort, we present a task-based recommendation approach that exploits aggregated developers’ interaction traces. Our novel approach, Consensus Task Interaction Trace Recommender (CITR), recommends file(s)-to-edit that help perform a set of tasks based on a tasks-related set of interaction traces obtained from developers who performed similar change tasks on the same or different custom instances of the same system. Our approach uses a consensus algorithm, which takes as input task-related interaction traces and recommends a consensus task interaction trace that developers can use to complete given similar change tasks that require editing (a) common file(s). To evaluate the efficiency of our approach, we perform three different evaluations. The first evaluation measures the accuracy of CITR recommendations. In the second evaluation, we assess to what extent CITR can help developers by conducting an observational controlled experiment in which two groups of developers performed evaluation tasks with and without the recommendations of CITR. In the third and last evaluation, we compare CITR to a state-of-the-art recommendation approach, MI. Results report with statistical significance that CITR can correctly recommend on average 73% of the files to be edited. Furthermore, they show that CITR can increase developers’ successful task completion rate. CITR outperforms MI by an average of 31% higher recommendation accuracy. |
| ArticleNumber | 147 |
| Author | Hamel, Sylvie Guéhéneuc, Yann-Gaël Sager, Pascal Etaiwi, Layan |
| Author_xml | – sequence: 1 givenname: Layan orcidid: 0000-0001-9250-7578 surname: Etaiwi fullname: Etaiwi, Layan email: mashael.etaiwi@polymtl.ca organization: Polytechnique Montréal – sequence: 2 givenname: Pascal surname: Sager fullname: Sager, Pascal organization: University of Zurich, Zurich University of Applied Sciences – sequence: 3 givenname: Yann-Gaël surname: Guéhéneuc fullname: Guéhéneuc, Yann-Gaël organization: Concordia University – sequence: 4 givenname: Sylvie surname: Hamel fullname: Hamel, Sylvie organization: Université de Montréal |
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| ContentType | Journal Article |
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| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Recommendation systems Task-related interaction traces Maintenance Software navigation Mylyn interaction traces Consensus algorithm |
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| References_xml | – reference: Fagin R, Kumar R, Mahdian M, Sivakumar D, Vee E (2004) Comparing and aggregating rankings with ties. In: Proceedings of the Twenty-Third ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, Association for Computing Machinery, New York, NY, USA, PODS ’04, pp 47–58,https://doi.org/10.1145/1055558.1055568 – reference: Ailon N, Charikar M, Newman A (2008) Aggregating inconsistent information: Ranking and clustering. J ACM 55(5). https://doi.org/10.1145/1411509.1411513 – reference: Ali A, Meilă M (2012) Experiments with kemeny ranking: What works when? Math Soc Sci 64(1):28–40. https://doi.org/10.1016/j.mathsocsci.2011.08.008, computational Foundations of Social Choice – reference: RobillardMCoelhoWMurphyGHow effective developers investigate source code: an exploratory studyIEEE Trans Softw Eng200430128899010.1109/TSE.2004.101 – reference: Avazpour I, Pitakrat T, Grunske L, Grundy J (2014) Dimensions and Metrics for Evaluating Recommendation Systems. In: Robillard MP, Maalej W, Walker RJ, Zimmermann T (eds) Recommendation systems in software engineering, Springer Berlin Heidelberg, pp 245–273. https://doi.org/10.1007/978-3-642-45135-5_10 – reference: Wang J, Peng X, Xing Z, Zhao W (2011) An exploratory study of feature location process: Distinct phases, recurring patterns, and elementary actions. In: 2011 27th IEEE international conference on software maintenance (ICSM), pp 213–22https://doi.org/10.1109/ICSM.2011.6080788 – reference: Starke J, Luce C, Sillito J (2009) Searching and skimming: An exploratory study. 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| Title | Consensus task interaction trace recommender to guide developers’ software navigation |
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