A Hierarchical Topical Modeling Approach for Recommending Repair of Quality Bugs. Krasniqi, R. & Do, H. In 2023 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pages 37–48, Taipa, Macao, March, 2023. IEEE. ![A Hierarchical Topical Modeling Approach for Recommending Repair of Quality Bugs [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 32 downloads Quality bugs are difficult to detect because the implemented quality-related features are commonly scattered across the codebase. Unfortunately, this scattered information prevents software developers from holistically understanding the root cause of quality bugs. The traditional view of a system does not support a hierarchical code view for monitoring and tracing how quality features are topically related and how they interact with each other. In this paper, we show how these limitations can be overcome by leveraging a Hierarchical Dirichlet Process (HDP) topic modeling technique along with other supporting intermediary techniques such as structural and textual analyses to capture hierarchical topical relationships among quality features across the codebase that yield to detection of quality bugs. We present SoftQualTopicDetector, that is capable of clustering scattered quality concerns into a meaningful hierarchy to infer a set of candidate classes relevant for recommending repair of quality bugs. The higher the ranking of classes into a hierarchy the more relevant they are regarded to contain information about the bug under investigation. Additionally, SoftQualTopicDetector incorporates three rich visualization features for monitoring, prioritizing, and 3-D tracing of suspicious classes to enhance aspects of maintainability, functional suitability, and traceability. We conduct an empirical evaluation of SoftQualTopicDetector that shows an improvement over the baseline and the state-of-the-art by terms ≈17% and in of average precision and ≈21% recall respectively.
@inproceedings{krasniqi_hierarchical_2023,
address = {Taipa, Macao},
title = {A {Hierarchical} {Topical} {Modeling} {Approach} for {Recommending} {Repair} of {Quality} {Bugs}},
isbn = {9781665452786},
url = {https://ieeexplore.ieee.org/document/10123610/},
doi = {10.1109/SANER56733.2023.00014},
abstract = {Quality bugs are difficult to detect because the implemented quality-related features are commonly scattered across the codebase. Unfortunately, this scattered information prevents software developers from holistically understanding the root cause of quality bugs. The traditional view of a system does not support a hierarchical code view for monitoring and tracing how quality features are topically related and how they interact with each other. In this paper, we show how these limitations can be overcome by leveraging a Hierarchical Dirichlet Process (HDP) topic modeling technique along with other supporting intermediary techniques such as structural and textual analyses to capture hierarchical topical relationships among quality features across the codebase that yield to detection of quality bugs. We present SoftQualTopicDetector, that is capable of clustering scattered quality concerns into a meaningful hierarchy to infer a set of candidate classes relevant for recommending repair of quality bugs. The higher the ranking of classes into a hierarchy the more relevant they are regarded to contain information about the bug under investigation. Additionally, SoftQualTopicDetector incorporates three rich visualization features for monitoring, prioritizing, and 3-D tracing of suspicious classes to enhance aspects of maintainability, functional suitability, and traceability. We conduct an empirical evaluation of SoftQualTopicDetector that shows an improvement over the baseline and the state-of-the-art by terms ≈17\% and in of average precision and ≈21\% recall respectively.},
language = {en},
urldate = {2023-07-20},
booktitle = {2023 {IEEE} {International} {Conference} on {Software} {Analysis}, {Evolution} and {Reengineering} ({SANER})},
publisher = {IEEE},
author = {Krasniqi, Rrezarta and Do, Hyunsook},
month = mar,
year = {2023},
keywords = {Conference Full Papers},
pages = {37--48},
}
Downloads: 32
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