Empirical Analysis of Artificial Immune System Algorithms for Aging Related Bug Prediction

Khanna, Megha; Aggarwal, Mehak; Singhal, Naman

doi:10.1109/icaccs51430.2021.9441809

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…F-score is a combination of precision and sensitivity, as shown in Equation ( 15) [65]. Geometric mean (G-mean) refers to the square root of the product of sensitivity and specificity [66], as given in Equation ( 16).…”

Section: A Evaluation Of the Trained ML Modelsmentioning

confidence: 99%

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Alablani

Arafah²

2023

IEEE Access

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Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

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Section: A Evaluation Of the Trained ML Modelsmentioning

confidence: 99%

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Alablani

Arafah²

2023

IEEE Access

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“…Khanna and her team have used Artificial Immune Systems for developing SA bugs prediction classifiers [17]. They have used five different types of open-source SW systems to validate the proposed models and asserted that these models have the ability to predict SA bugs.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Framework for Software Aging-Related Bug Prediction using Weighted Extreme Learning Machine

Kumar,

Singh,

Murthy

et al. 2023

Annals of Computer Science and Information Systems

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Software ageing (SA) related bugs highlight the issue of software failure within continuously running systems, resulting in a decline in quality, system crashes, resource misuse, and more. To mitigate these bugs, software companies employ various techniques, including code reviews, bug-tracking systems deployment, and thorough testing. Nevertheless, the identification of aging-related bugs remains challenging through these conventional approaches. To address this predicament, early prediction of the affected software regions due to runtime failures can be immensely valuable for software quality assurance teams. By accurately identifying the vulnerable areas, these teams can strategically allocate their limited resources during the testing and maintenance processes. This proactive approach ensures a more efficient and effective bug detection and resolution, enhancing overall software reliability and performance. This study aims to develop aging-related bug prediction models using source code metrics as input. In particular, our objective is to investigate metrics selections, data balancing, and weighted ELM to detect software runtime failure. Experimental results show that ELM with data imbalance SMOTE technique performs the best compared to weighted ELM for addressing the class imbalance problem. The weighted ELM and ELM + SMOTE can predict SA bugs, and these models can be applied to the future releases of software projects for online failure prediction well in advance. The experimental finding shows that the models trained using normal ELM with SMOTE data sampling techniques have significant performance improvement.

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Analysis of Search Based Algorithms for Prediction of Aging Related Bugs

Bansal,

Khanna,

Dhawan

et al. 2023

Lecture Notes in Networks and Systems

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Empirical Analysis of Artificial Immune System Algorithms for Aging Related Bug Prediction

Cited by 3 publications

References 19 publications

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

An Empirical Framework for Software Aging-Related Bug Prediction using Weighted Extreme Learning Machine

Analysis of Search Based Algorithms for Prediction of Aging Related Bugs

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