Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities o...

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Autor principal:	Abdulaziz Alhumam
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fault localization neural ranking parameter selection Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/1cc1339545bc42fe9c2bc1dbd2d9bdb0
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id	oai:doaj.org-article:1cc1339545bc42fe9c2bc1dbd2d9bdb0
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spelling	oai:doaj.org-article:1cc1339545bc42fe9c2bc1dbd2d9bdb02021-11-11T19:19:27ZSoftware Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection10.3390/s212174011424-8220https://doaj.org/article/1cc1339545bc42fe9c2bc1dbd2d9bdb02021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7401https://doaj.org/toc/1424-8220The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.Abdulaziz AlhumamMDPI AGarticlefault localizationneural rankingparameter selectionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7401, p 7401 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fault localization neural ranking parameter selection Chemical technology TP1-1185
spellingShingle	fault localization neural ranking parameter selection Chemical technology TP1-1185 Abdulaziz Alhumam Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
description	The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.
format	article
author	Abdulaziz Alhumam
author_facet	Abdulaziz Alhumam
author_sort	Abdulaziz Alhumam
title	Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
title_short	Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
title_full	Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
title_fullStr	Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
title_full_unstemmed	Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection
title_sort	software fault localization through aggregation-based neural ranking for static and dynamic features selection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1cc1339545bc42fe9c2bc1dbd2d9bdb0
work_keys_str_mv	AT abdulazizalhumam softwarefaultlocalizationthroughaggregationbasedneuralrankingforstaticanddynamicfeaturesselection
_version_	1718431512572985344

Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

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