Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering

San, Kyawt Kyawt; Washizaki, Hironori; Fukazawa, Yoshiaki; Honda, Kiyoshi; Taga, Masahiro; Matsuzaki, Akinobu

doi:10.3390/math9222945

Cited by 4 publications

(4 citation statements)

References 29 publications

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“…After each iteration of the genetic algorithm, the next gen-eration of the population is generated. The offspring need to be evaluated using a fitness function [32], [33]. The fitness function assesses the proximity of an individual solution to the ideal solution [34], and we use Mean Squared Error (MSE) as the criterion for the fitness function.…”

Section: Individual Evaluatingmentioning

confidence: 99%

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

FU,

RYU,

KIM

2024

IEICE Trans. Inf. & Syst.

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In the software testing phase, software reliability growth models (SRGMs) are commonly used to evaluate the reliability of software systems. Traditional SRGMs are restricted by their assumption of a continuous growth pattern for the failure detection rate (FDR) throughout the testing phase. However, the assumption is compromised by Change-Point phenomena, where FDR fluctuations stem from variations in testing personnel or procedural modifications, leading to reduced prediction accuracy and compromised software reliability assessments. Therefore, the objective of this study is to improve software reliability prediction using a novel approach that combines genetic algorithm (GA) and deep learning-based SRGMs to account for the Change-point phenomenon. The proposed approach uses a GA to dynamically combine activation functions from various deep learning-based SRGMs into a new mutated SRGM called MuSRGM. The MuSRGM captures the advantages of both concave and S-shaped SRGMs and is better suited to capture the change-point phenomenon during testing and more accurately reflect actual testing situations. Additionally, failure data is treated as a time series and analyzed using a combination of Long Short-Term Memory (LSTM) and Attention mechanisms. To assess the performance of MuSRGM, we conducted experiments on three distinct failure datasets. The results indicate that MuSRGM outperformed the baseline method, exhibiting low prediction error (MSE) on all three datasets. Furthermore, MuSRGM demonstrated remarkable generalization ability on these datasets, remaining unaffected by uneven data distribution. Therefore, MuSRGM represents a highly promising advanced solution that can provide increased accuracy and applicability for software reliability assessment during the testing phase.

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Section: Individual Evaluatingmentioning

confidence: 99%

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

FU,

RYU,

KIM

2024

IEICE Trans. Inf. & Syst.

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“…While San et al [54] presented a new technique for software program reliability modeling known as deep projects software reliability growth model deep cross-project software reliability growth model and this approach is a cross-project forecasting approach that makes use of the elements of previous tasks records via challenge similarity. Specifically, the proposed technique applies block-based mission resolution of coaching and modeling statistics supply by using a deep mastering method.…”

Section: Previous Studiesmentioning

confidence: 99%

A survey of predicting software reliability using machine learning methods

Khaleel,

Salih

2024

IJ-AI

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<span lang="EN-US">In light of technical and technological progress, software has become an urgent need in every aspect of human life, including the medicine sector and industrial control. Therefore, it is imperative that the software always works flawlessly. The information technology sector has witnessed a rapid expansion in recent years, as software companies can no longer rely only on cost advantages to stay competitive in the market, but programmers must provide reliable and high-quality software, and in order to estimate and predict software reliability using machine learning and deep learning, it was introduced A brief overview of the important scientific contributions to the subject of software reliability, and the researchers' findings of highly efficient methods and techniques for predicting software reliability. </span>

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“…Zhu [22] introduced the concept of complex reliability, which considered both hardware and software components, and proposed maintenance policies applicable to such systems. Several recent software reliability studies have employed machine-learning and deep-learning techniques [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

Lee,

Chang,

Pham

2023

Mathematics

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The coronavirus disease (COVID-19) outbreak has prompted various industries to embark on digital transformation efforts, with software playing a critical role. Ensuring the reliability of software is of the utmost importance given its widespread use across multiple industries. For example, software has extensive applications in areas such as transportation, aviation, and military systems, where reliability problems can result in personal injuries and significant financial losses. Numerous studies have focused on software reliability. In particular, the software reliability growth model has served as a prominent tool for measuring software reliability. Previous studies have often assumed that the testing environment is representative of the operating environment and that software failures occur independently. However, the testing and operating environments can differ, and software failures can sometimes occur dependently. In this study, we propose a new model that assumes uncertain operating environments and dependent failures. In other words, the model proposed in this study takes into account a wider range of environments. The numerical examples in this study demonstrate that the goodness of fit of the new model is significantly better than that of the existing SRGM. Additionally, we show the utilization of the sequential probability ratio test (SPRT) based on the new model to assess the reliability of the dataset.

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Deep Cross-Project Software Reliability Growth Model Using Project Similarity-Based Clustering

Cited by 4 publications

References 29 publications

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

MuSRGM: A Genetic Algorithm-Based Dynamic Combinatorial Deep Learning Model for Software Reliability Engineering

A survey of predicting software reliability using machine learning methods

Study of a New Software Reliability Growth Model under Uncertain Operating Environments and Dependent Failures

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