Software Test Effort Estimation Research Articles

An Empirical Study on Software Test Effort Estimation for Defense Projects

Effort estimation of software testing plays a vital role in the effective completion of testing. In particular, software test effort estimation in defense projects is not an easy and simple phenomenon, owing to internal and external factors. This study aims to investigate the relationships between software test metrics used in the industry and software testing efforts. A method for estimating the testing effort is proposed using a novel set of software testing metrics that have not been used in any previously proposed software test effort estimation methods. In this study, 15 completed software projects of a CMMI Level-3 certified defense industry company were analyzed. The results of the empirical study show that the proposed method with the given metrics provides acceptable prediction quality, with Pred (0.25) and Pred (0.30) values equal to 0.867. We obtained plausible results and demonstrated that our newly proposed metrics can be safely used for software test effort estimation.

Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network

In present scenario, the software companies are frequently involving software test effort estimation to allocate the resources efficiently during the software development process. Different machine learning models are developed to estimate the total effort that would be required before the software product could be delivered. These computational models are used to use the past data to estimate the efforts. In the current studies, test effort estimation for software is predicted using the Genetic algorithm and Neural Network. The attributes are selected using the Genetic algorithm and similarity measure between the attribute values has been computed using the Cosine Similarity measure. The simulation experiments were done using the PROMISE and Kaggle repository and implementation was done using the MATLAB software. The performance metrics namely, precision, recall, and accuracy are computed to evaluate against the existing techniques. The accuracy of the proposed model is 91.3% and results are improved by 8.9% in comparison to existing technique and comparison has been made for superiority to predict the test effort for software development.

A Model to Measure Software Testing Effort Estimation in the Integrated Environment of ERNN, BMO & PSO

A Model to Measure Software Testing Effort Estimation in the Integrated Environment of ERNN, BMO & PSO

A MULTIPLE NON-LINEAR REGRESSION MODEL TO ESTIMATE THE AGILE TESTING EFFORTS FOR SMALL WEB PROJECTS

Context. Software testing effort estimation is one of the important problems in software development and software testing life cycle. The object of the study is the process of estimating the agile testing efforts for small Web projects. The subject of the study is the multiple regression models for estimating the agile testing efforts for small Web projects. Objective. The goal of the work is the creation of the multiple non-linear regression model for estimating the agile testing efforts for small Web projects on the basis of the Johnson multivariate normalizing transformation. Method. The model, confidence and prediction intervals of multiple non-linear regression for estimating the agile testing efforts for small Web projects are constructed on the basis of the Johnson multivariate normalizing transformation for non-Gaussian data with the help of appropriate techniques. The techniques based on the multiple non-linear regression analysis using the multivariate normalizing transformations to build the models, equations, confidence and prediction intervals of multiple non-linear regressions are used. The techniques allow to take into account the correlation between random variables in the case of normalization of multivariate non-Gaussian data. In general, this leads to a reduction of the mean magnitude of relative error, the widths of the confidence and prediction intervals in comparison with the linear models and nonlinear models constructed using univariate normalizing transformations. Results. Comparison of the constructed model with the linear model and non-linear regression models based on the decimal logarithm and the Johnson univariate transformation has been performed. Conclusions. The multiple non-linear regression model to estimate the agile testing efforts for small Web projects is firstly constructed on the basis of the Johnson multivariate transformation for SB family. This model, in comparison with other regression models (both linear and non-linear), has a smaller value of the mean magnitude of relative error, smaller widths of the confidence and prediction intervals. The prospects for further research may include the application of other multivariate normalizing transformations and data sets to construct the multiple non-linear regression model for estimating the agile testing efforts for small Web projects.

Majority vote feature selection algorithm in software fault prediction

Identification and location of defects in software projects is an important task to improve software quality and to reduce software test effort estimation cost. In software fault prediction domain, it is known that 20% of the modules will in general contain about 80% of the faults. In order to minimize cost and effort, it is considerably important to identify those most error prone modules precisely and correct them in time. Machine Learning (ML) algorithms are frequently used to locate error prone modules automatically. Furthermore, the performance of the algorithms is closely related to determine the most valuable software metrics. The aim of this research is to develop a Majority Vote based Feature Selection algorithm (MVFS) to identify the most valuable software metrics. The core idea of the method is to identify the most influential software metrics with the collaboration of various feature rankers. To test the efficiency of the proposed method, we used CM1, JM1, KC1, PC1, Eclipse Equinox, Eclipse JDT datasets and J48, NB, K-NN (IBk) ML algorithms. The experiments show that the proposed method is able to find out the most significant software metrics that enhances defect prediction performance.

Software reliability modelling using neural network with compounded decreased error rate

The main task for the software management is to predict the reliability of a software by using an appropriate software reliability growth model (SRGM). The accuracy of the mechanism adopted for reliability prediction is also an important factor. In this study, a software reliability growth modelling problem has been solved using artificial neural networks (ANNs), as ANNs based models are competitive with traditional statistical models for software testing effort estimation. In the first phase, a software reliability growth model has been proposed with compounded decreased error rate to predict the software reliability. The model is further extended by incorporating Cobb-Douglas production function to model the combined effect of time and resources used. Multi-layer feed-forward neural network is used to solve the problems. The weights associated with various input parameters are updated using generalised delta rule and Levenberg-Marquardt method. A comparison is then made with the results from existing models in the literature. The work is validated by using two famous real data sets. Numerical results show that the proposed models perform better in comparison to others existing in the literature.

Software reliability modelling using neural network with compounded decreased error rate

The main task for the software management is to predict the reliability of a software by using an appropriate software reliability growth model (SRGM). The accuracy of the mechanism adopted for reliability prediction is also an important factor. In this study, a software reliability growth modelling problem has been solved using artificial neural networks (ANNs), as ANNs based models are competitive with traditional statistical models for software testing effort estimation. In the first phase, a software reliability growth model has been proposed with compounded decreased error rate to predict the software reliability. The model is further extended by incorporating Cobb-Douglas production function to model the combined effect of time and resources used. Multi-layer feed-forward neural network is used to solve the problems. The weights associated with various input parameters are updated using generalised delta rule and Levenberg-Marquardt method. A comparison is then made with the results from existing models in the literature. The work is validated by using two famous real data sets. Numerical results show that the proposed models perform better in comparison to others existing in the literature.

A model for software defect prediction using support vector machine based on CBA

Software defection prediction is not only crucial for improving software quality, but also helpful for software test effort estimation. As is well-known, 80% of the fault happens in 20% of the modules. Therefore, we need to find out the most error prone modules accurately and correct them in time to save time, money, and energy. Support vector machine SVM is an advanced classification method that fits the defection classification. However, studies show that, the value of parameters of SVM model has a remarkable influence on its classification accuracy and the selection process lacks theory guidance that makes the SVM model uncertainty and low efficiency. In this paper, a CBA-SVM software defect prediction model is proposed, which take advantage of the non-linear computing ability of SVM model and optimisation capacity of bat algorithm with centroid strategy CBA. Through the experimental comparison with other models, CBA-SVM is proved to have a higher accuracy.

Estimation of software testing effort using fuzzy multiple linear regression

Prior to the development of any software project, an estimate of the testing effort involved in it needs to be determined. The estimation is very important because the optimisation of the costs associated with the development depends on the accuracy of the estimation. Due to complex and varied nature of software applications and associated uncertainty, prediction of the cost of software development at an early stage is a tough job. In this research, by using a widely used algorithmic model COCOMO II, fuzzy logic and fuzzy multiple linear regression techniques, we provide an algorithm for software testing effort estimation.

SOFTWARE TESTING EFFORT ESTIMATION WITH COBB-DOUGLAS FUNCTION: A PRACTICAL APPLICATION

Effort estimation is one of the critical challenges in Software Testing Life Cycle (STLC). It is the basis for the project’s effort estimation, planning, scheduling and budget planning. This paper illustrates model with an objective to depict the accuracy and bias variation of an organization’s estimates of software testing effort through Cobb-Douglas function (CDF). Data variables selected for building the model were believed to be vital and have significant impact on the accuracy of estimates. Data gathered for the completed projects in the organization for about 13 releases. Statistically, all variables in this model were statistically significant at p<0.05 and p<0.01 levels. The Cobb-Douglas function was selected and used for the software testing effort estimation. The results achieved with CDF were compared with the estimates provided by the area expert. The model’s estimation figures are more accurate than the expert judgment. CDF has one of the appropriate techniques for estimating effort for software testing. CDF model accuracy is 93.42%. Index Terms: Effort estimation, software testing, Cobb-Douglas function, STLC and SDLC

Software test effort estimation: a model based on cuckoo search

Test effort estimation is the process of predicting effort for testing the software. It has always been a fascinating area for software engineering researchers. “How long will it take to test the system?” is the most promising question in minds of testers before the testing process actually starts. Many factors such as the productivity of the test team, strategy chosen for testing, the size and complexity of the system, technical factors, and expected quality can affect test effort estimation. Testing requires a good amount of time and effort in the entire software development life cycle. Several researches have attempted to develop test effort estimation models but still it is not possible to achieve accurate forecasting. A new model based on a metaheuristic technique called, cuckoo search, for estimating the test effort is proposed in this paper. The proposed model is used to assign weights to the various factors involved based on past results, and, is then used for predicting the test effort for new projects of similar kind.