Wireless networks such as MANETs present unique challenges due to their dynamic and decentralized nature. Efficient routing protocols are essential for achieving reliable and robust communication in such networks. In ...
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Automatic Speech Recognition (ASR) system is an emerging technology used in various fields such as robotics, traffic controls, and healthcare, etc. The leading cause of ASR performance degradation is mismatch between ...
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The emergence of Internet of Things (IoT) has paved the way to realize ubiquitous access to manifold services and applications. Effective utilization of these services vitally demands secure deployment of IoT systems ...
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Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebase...
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Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l
Railway accidents are an under-scrutinised cause of death in India. Train accidents are caused by various consequences of collisions, derailments, signal errors and so on. Furthermore, when train derailments become di...
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With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent C...
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Software bug repositories stores a wealth of information related to the problems that occurred during the software development. Today’s software development is a modular approach, with multiple developers working in ...
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Perceptual image hashing is a significant and time-effective method for recognizing images within extensive databases, focusing on achieving two key objectives: robustness and discrimination. The right balance between...
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An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in suc...
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The ever-growing amount of data generated by modern networks poses significant challenges for intrusion detection systems (IDS) in effectively analyzing and classifying security risks. Therefore, it is crucial to iden...
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