Implementation of Machine Learning Techniques in Software Reliability: A framework

Banga, Manu; Bansal, Abhay; Singh, Archana

doi:10.1109/icactm.2019.8776830

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…ANN has been taken as the baseline model, and it has been found that the Long Short-Term Memory (LSTM)model performs well. Banga, et al [22] and Bisi and Goyal [13] introduced an approach that is used to find the most relevant parameter affecting software reliability. In this research, a hybrid approach is used to predict the fault of software with the help of machine learning.…”

Section: Literature Surveymentioning

confidence: 99%

Software reliability prediction using ensemble learning with random hyperparameter optimization

Habtemariam,

Mohapatra,

Seid

2024

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The paper investigates software reliability prediction by using ensemble learning with random hyperparameter optimization. Software reliability is a significant problem with software quality that developers face. It involves accurately predicting the next failure. In recent years, machine learning techniques and ensemble learning approaches have been applied to improve software reliability prediction. These approaches aim to analyze historical data and develop models that can accurately forecast when failures are likely to occur. The article proposes an ensemble learning regression model using Ridge, Bayesian Ridge, Support Vector Regressor (SVR), K-Nearest Neighbors Algorithm (KNN), Regression tree, Random Forest, Neural network, and Decision Tree as base learners. Ridge is used as a combiner model. Each base learner hyperparameter is tuned using a random search algorithm automatically. A random hyperparameter search optimization algorithm selects the hyperparameter and adjusts it for overfitting and underfitting. The base models are tuned to minimize bias and variance. The performances of the models are evaluated using standard error measures such as Mean Squared Error (MSE), Sum of Squared Error (SSE), and Normalized Root Mean Square Error (NRMSE). The proposed ensemble model is compared with existing models using a benchmark dataset. The Iyer,and Lee, and Musa datasets are used for the experiment. The dataset is scaled using standard methods like logarithmic scaling, lagging, and linear interpolation. The results of the statistical comparison show better performance by our proposed model as compared to existing models.

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Section: Literature Surveymentioning

confidence: 99%

Software reliability prediction using ensemble learning with random hyperparameter optimization

Habtemariam,

Mohapatra,

Seid

2024

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“…where the function a(t) and represents the expected number of initial failures and newly introduced errors until the start point of testing period and b(t) represents the failure detection rate per fault. This paper presents a specific software reliability model with consideration of the uncertainty of the operating environment based on the work by Pham [26].…”

Section: Existing Srgmsmentioning

confidence: 99%

“…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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“…Intelligent software becomes necessary by combining machine learning techniques on the defective company dataset to build reliable models in different dimensions. Based on the early prediction model [7].…”

Section: Introductionmentioning

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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Implementation of Machine Learning Techniques in Software Reliability: A framework

Cited by 7 publications

References 12 publications

Software reliability prediction using ensemble learning with random hyperparameter optimization

Software reliability prediction using ensemble learning with random hyperparameter optimization

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

A survey of predicting software reliability using machine learning methods

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