Structural approach to the estimation of the number of residual software faults based on the hyper-geometric distribution

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.

Section: Introductionmentioning

confidence: 99%

Software Bug Prediction using Machine Learning Approach

Hammouri¹,

Hammad²,

Alnabhan³

et al. 2018

“…1 2 75 24 2 8 2 0 31 25 1 15 3 30 63 26 7 31 4 13 128 27 0 1 5 13 122 28 22 57 6 3 27 29 2 27 7 17 136 30 5 35 8 2 49 310 5 10 15 20 25 30 35 40 45 A Test/Debug data set has 46 measurements is given in Table III. The data set was presented in [37]. The number of measurements collected during the testing process is small.…”

Section: Test/debug Datamentioning

confidence: 99%

“…Field report data was developed to measure system faults during testing in a real-time application [37]. The software system consists of 200 modules with each having one kilo line of code of FORTRAN.…”

Section: Test/debug Datamentioning

confidence: 99%

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

Sheta¹,

Kassaymeh²,

Rine³

2014

Abstract-On time and within budget software project development represents a challenge for software project managers. Software management activities include but are not limited to: estimation of project cost, development of schedules and budgets, meeting user requirements and complying with standards. Recruiting development team members is a sophisticated problem for a software project manager. Since the utmost cost in software development effort is manpower, software project effort and is associated cost estimation models are used in estimating the effort required to complete a project. This effort estimate can then be converted into dollars based on the proper labor rates. An initial development team needs to be selected not only at the beginning of the project but also during the development process. It is important to allocate the necessary team to a project and efficiently distribute their effort during the development life cycle. In this paper, we provide our initial idea of developing a prediction model for defining the estimated required number of test workers of a software project during the software testing process. The developed models utilize the test instance and the number of observed faults as input to the proposed models. Artificial Neural Networks (ANNs) successfully build the dynamic relationships between the inputs and output and produce and accurate predication estimates.

“…In the second case study, a real-time application [56] of a software system containing 200 modules of FORTRAN language was used to test our proposed methodology. The data consists of 111 measurements [55].…”

Section: B Test/debug Datamentioning

confidence: 99%

“…In our third case study, we used a Test/Debug data set including 46 measurements as presented in [56]. We ran the GWO to find the best parameters to tune the EXPM, POWM and DSSM.…”

Section: Test/debug Datamentioning

confidence: 99%

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

Sheta¹,

Abdel-Raouf²

2016

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.