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On the effect of incompleteness to check requirement-to-method traces

Authors:

Mouna Hammoudi,

Christoph Mayr-Dorn,

Alexander EgyedAuthors Info & Claims

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

Pages 1465 - 1474

https://doi.org/10.1145/3412841.3442021

Published: 22 April 2021 Publication History

Abstract

Requirement-to-method traces reveal the code location(s) where a requirement is implemented. This is helpful to software engineers when they have to perform tasks such as software maintenance or bug fixing. Indeed, being aware of the method(s) that implement a requirement saves engineers' time, as it pinpoints the exact code region that needs to be edited to perform a bug fix or a maintenance task. Engineers produce traces manually as well as automatically. Nevertheless, traces are incomplete. This limits the amount of information that could be used by an automated technique to check further traces. Therefore, since traces are incomplete, we would like to study the effect of incompleteness on the automated assessment of requirement-to-method traces. In this paper, we apply machine learning on either incomplete or complete tracing information and we evaluate the effect of incompleteness on checking trace information. We demonstrate that the use of complete traces might yield a higher precision but yields a lower recall. Also, the use of incomplete traces yields a higher recall but a lower precision.

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Cited By

Li XWang BWan HLi YJiang HDeng YZou Z(2024)MLTracer: An Approach Based on Multi-Layered Gradient Boosting Decision Trees for Requirements Traceability Recovery2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651499(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651499
Wang BLi XWan HDeng Y(2023)A Systematic Mapping Study of Machine Learning Techniques Applied to Software Traceability2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394446(623-628)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394446
Wang BWang ZWan HLi XDeng Y(2023)An Empirical Study on Data Balancing in Machine Learning Based Software Traceability Methods2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191386(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191386
Show More Cited By

Index Terms

On the effect of incompleteness to check requirement-to-method traces
1. Software and its engineering
  1. Software creation and management

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Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied Computing

March 2021

2075 pages

ISBN:9781450381048

DOI:10.1145/3412841

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University
,
Jiman Hong
Soongsil University, South Korea
,
Program Chairs:
Alessio Bechini
University of Pisa, Italy
,
Eunjee Song
Baylor University

Copyright © 2021 Owner/Author.

This work is licensed under a Creative Commons Attribution-ShareAlike International 4.0 License.

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 April 2021

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Austrian Science Fund (FWF)
Federal Ministry of Transport, Innovation and Technology
Provinces of Upper Austria and Styria
Austrian Federal Ministry for Digital and Economic Affairs

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SAC '21

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SIGAPP

SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing

March 22 - 26, 2021

Virtual Event, Republic of Korea

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Li XWang BWan HLi YJiang HDeng YZou Z(2024)MLTracer: An Approach Based on Multi-Layered Gradient Boosting Decision Trees for Requirements Traceability Recovery2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651499(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651499
Wang BLi XWan HDeng Y(2023)A Systematic Mapping Study of Machine Learning Techniques Applied to Software Traceability2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394446(623-628)Online publication date: 1-Oct-2023
https://doi.org/10.1109/SMC53992.2023.10394446
Wang BWang ZWan HLi XDeng Y(2023)An Empirical Study on Data Balancing in Machine Learning Based Software Traceability Methods2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191386(1-8)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191386
Zhang SWan HXiao YLi Z(2022)IRRT: An Automated Software Requirements Traceability Tool based on Information Retrieval Model2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C57518.2022.00084(525-532)Online publication date: Dec-2022
https://doi.org/10.1109/QRS-C57518.2022.00084
Hammoudi MMayr-Dorn CMashkoor AEgyed A(2021)A Traceability Dataset for Open Source Systems2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)10.1109/MSR52588.2021.00073(555-559)Online publication date: May-2021
https://doi.org/10.1109/MSR52588.2021.00073
Hammoudi MMayr-Dorn CMashkoor AEgyed A(2021)TraceRefiner: An Automated Technique for Refining Coarse-Grained Requirement-to-Class Traces2021 28th Asia-Pacific Software Engineering Conference (APSEC)10.1109/APSEC53868.2021.00009(12-21)Online publication date: Dec-2021
https://doi.org/10.1109/APSEC53868.2021.00009

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