Abstract
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.
Highlights
It is hard for developers to find and correct software flaws while the program is being developed and after its release
The exam score is defined as the percentage of code that has to be inspected until the first statement in which the problem is located is reached during the examination process
The exam score (ES) is often used in many research to assess the efficacy of a fault localization method [43,44]
Summary
It is hard for developers to find and correct software flaws while the program is being developed and after its release. The complete process of bug identification is timeconsuming, and the automated software does the job perfectly through fault localization. Using fault localization models reduces the debugging expenses and allows the developers to devote more to possible vulnerable components of the software. Fault localization, which focuses on locating defects in software manually, has been a difficult, time-consuming, and costly job because of the complexity of large-scale software systems [1]. As a result of the constraints mentioned above, interest has been revived in creating automated models for localization of faults in software while at the same time minimizing human involvement. A fault in the computer program is defined as any inappropriate move, procedure, or data specification, sometimes termed the bug. When the software fails to operate as expected, there must be a possible bug in the code, and if such bugs or faults are ignored, the complete software may fail
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