Parameter estimation of hyper-geometric distribution software reliability growth model by genetic algorithms

Minohara, Takashi; Tohma, Y.

doi:10.1109/issre.1995.497673

Cited by 35 publications

(20 citation statements)

References 5 publications

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“…DS2 has 111 measurements. DS3 is developed in [2], which contains real measured data for a test/debug program of a realtime control application presented in [18]. Tables I to III present DS1, DS2 and DS3, respectively.…”

Section: Datasets and Evaluation Methodologymentioning

confidence: 99%

Software Bug Prediction using Machine Learning Approach

Hammouri¹,

Hammad²,

Alnabhan³

et al. 2018

ijacsa

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Abstract-Software Bug Prediction (SBP) is an important issue in software development and maintenance processes, which concerns with the overall of software successes. This is because predicting the software faults in earlier phase improves the software quality, reliability, efficiency and reduces the software cost. However, developing robust bug prediction model is a challenging task and many techniques have been proposed in the literature. This paper presents a software bug prediction model based on machine learning (ML) algorithms. Three supervised ML algorithms have been used to predict future software faults based on historical data. These classifiers are Naïve Bayes (NB), Decision Tree (DT) and Artificial Neural Networks (ANNs). The evaluation process showed that ML algorithms can be used effectively with high accuracy rate. Furthermore, a comparison measure is applied to compare the proposed prediction model with other approaches. The collected results showed that the ML approach has a better performance.

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Section: Datasets and Evaluation Methodologymentioning

confidence: 99%

Software Bug Prediction using Machine Learning Approach

Hammouri¹,

Hammad²,

Alnabhan³

et al. 2018

ijacsa

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“…A real-time control application presented in [54], [55] is adopted as the first case study with a daily collected data. In Figure 1 (a), we show the actual and estimated accumulated failures curves for the EXPM, POWM and DSSM and the convergence behavior curves of the GWO process for the three developed models.…”

Section: A Test/debug Datamentioning

confidence: 99%

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

Sheta¹,

Abdel-Raouf²

2016

ijacsa

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Abstract-In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process. In this paper we explore the advantages of the Grey Wolf Optimization (GWO) algorithm in estimating the SRGM's parameters with the objective of minimizing the difference between the estimated and the actual number of failures of the software system. We evaluated three different software reliability growth models: the Exponential Model (EXPM), the Power Model (POWM) and the Delayed S-Shaped Model (DSSM). In addition, we used three different datasets to conduct an experimental study in order to show the effectiveness of our approach.

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“…Evolutionary computation techniques have been used successfully to solve various software reliability problems. Genetic algorithms have been used to estimate the parameters of the hyper geometric distribution model [30]. Genetic programming has also been explored in relation to its potential to solve software testing and reliability problems.…”

Section: Evolutionary Computationmentioning

confidence: 99%

Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models

Alweshah¹,

Ahmed²,

Aldabbas³

2015

IJCA

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Building reliability growth models to predict software reliability and identify and remove errors is both a necessity and a challenge for software testing engineers and project managers. Being able to predict the number of faults in software helps significantly in determining the software release date and in effectively managing project resources. Most of the growth models consider two or three parameters to estimate the accumulated faults in the testing process. Interest in using evolutionary computation to solve prediction and modeling problems has grown in recent years. In this paper, we explore the use of genetic programming (GP) as a tool to help in building growth models that can accurately predict the number of faults in software early on in the testing process. The proposed GP model is based on a recursive relation derived from the history of measured faults. The developed model is tested on real-time control, military, and operating system applications. The dataset was developed by John Musa of Bell Telephone Laboratories. The results of a comparison of the GP model with the traditional and simpler auto-regression model are presented.

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Parameter estimation of hyper-geometric distribution software reliability growth model by genetic algorithms

Cited by 35 publications

References 5 publications

Software Bug Prediction using Machine Learning Approach

Software Bug Prediction using Machine Learning Approach

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm

Evolution of Software Reliability Growth Models: A Comparison of Auto-Regression and Genetic Programming Models

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