Comparative evaluation of genetic algorithm-based test case optimization

Software testing is a crucial phase in software development process although it consumes more time and cost of software development. Researchers have proposed several approaches focusing on helping software testers to reduce the execution time and cost of the testing process. Test case optimization is a multi-objective approach that has become one of the best solutions to overcome these problems. Test case optimization focusing on reducing the number of test cases in the test suite that may reduce the overall testing time, cost and effort of software testers especially in regression testing. This paper presents the comparative evaluation between test case optimization techniques that are based on Genetic Algorithm (GA). The evaluation is based on five criteria i.e. technique objectives, applied fitness function, contributions, the percentage of the reduced test cases, fault detection capability, and technique limitations. The evaluation results able identify the gaps in the existing GAbased test case optimization approaches and provide insight in determining the potential research directions in this area.Keywords: Test case optimization, regression testing, multi-objectives, genetic algorithm, software testin

Group testing models with unknown link function

Group testing through the use of pooling has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest such as infection status. A topic of key interest in this area involves the development of regression models that relate the individual level covariates to the binary pool testing responses. The research in this area has primarily focused on parametric regression models. In this poster, we will introduce a new estimation method which can handle multi-dimensional covariates while assuming the link is unknown. The asymptotic properties of our estimators are also presented. We investigate the performance of our method through simulation and by applying it to a hepatitis data set obtained from the National Health and Nutrition Examination Survey

The use of Rasch analysis as a tool in the construction of a preference based measure: the case of AQLQ

The majority of quality of life instruments are not preference-based measures and so cannot be used within cost utility analysis. The Asthma Quality of Life Questionnaire (AQLQ) is one such instrument. The aim of this study was to develop a health state classification that is amenable to valuation from the AQLQ. Rasch models were applied to samples of responders to the AQLQ with the aim of i) selecting a number of items for a preference based utility measure (AQL-5D), ii) reducing the number of levels for each item to a more manageable number of levels for establishing AQL-5D. Selection of items for the evaluation survey was supported with conventional psychometric criteria for item selection (feasibility, internal consistency, floor and ceiling effects, responsiveness and regression against overall health). The role of Rasch analysis in reducing the number of item levels to a preconceived target number of levels proved unsuccessful. However, Rasch analysis proved to be a useful tool in assisting in the initial process of selecting items from an existing HRQL instrument in the construction of AQL-5D. The method is recommended for use alongside conventional psychometric testing to aid in the development of preference-based measures

Regression test cases selection for object-oriented programs based on affected statement

One of the most important activities in software maintenance is Regression testing. The re-execution of all test cases during the regression testing is costly. And even though several of the code based proposed techniques address procedural programs, so many of them can’t be use directly on object-oriented programs. This paper presents modification-revealing test case selection for regression testing of object-oriented software using dependence graph model analysis of the source code. The experimental evaluation of our proposed approach was done using nine programs. We measured the performances of our selection approach using precision and inclusiveness metrics. It was observed from the results that our approach increase the efficiency and effectiveness of regression testing in term of precision and inclusiveness. It was concluded that selection of modification-revealing test cases based on affected statements provides considerably better results for precision and inclusiveness compared to retest-all and random technique, and reducing the cost of regression testing

The Impact of Supply Chain Strategy on Real Earnings Quality and Market Added Value

Abstract The previous researches concluded that, the investor had to pressure on management for improving the earning quality, because it related with predicting the future prospect with high accuracy. This paper presents the impact of applying the supply chain management on the cost management to optimize productivity and decreasing cost which will have a direct impact on the earnings. The supply chain policy has advantages in reducing out the cost of capital, so that this policy could be used as a moderated variables. This research used the moderated multiple regression by collecting all data on the listed company in industry manufacturing from 2015-2017. In calculating the market value, this research used the Model H (Two Stages Model of Growth) in predicting the estimated price, which had fulfilled the minimum tracking signals criteria. The statistical testing shown out the supply chain have the positive impact on real earnings quality and market added value, where the higher earnings quality have made  the investors  decision in estimating the future prospect precisely. The supply chain policy played a critical role in reducing out the pattern of opportunistic behavior, when is a pressure on management in having proclivity to improve the earnings quality. This empirical testing had pointed out that publication financial earnings had been implication of game theory. To support the game theory in publication financial performance, this research had provided  a novelty in estimating investor s action  by providing out the schema of investors perception and accounting information. The supply chain policy had proclivity to give a push on supply chain management in implementing earning management, because management effort in disseminate the positive signal

Reducing regression test size by exclusion.

Operational software is constantly evolving. Regression testing is used to identify the unintended consequences of evolutionary changes. As most changes affect only a small proportion of the system, the challenge is to ensure that the regression test set is both safe (all relevant tests are used) and unclusive (only relevant tests are used). Previous approaches to reducing test sets struggle to find safe and inclusive tests by looking only at the changed code. We use decomposition program slicing to safely reduce the size of regression test sets by identifying those parts of a system that could not have been affected by a change; this information will then direct the selection of regression tests by eliminating tests that are not relevant to the change. The technique properly accounts for additions and deletions of code. We extend and use Rothermel and Harrold’s framework for measuring the safety of regression test sets and introduce new safety and precision measures that do not require a priori knowledge of the exact number of modification-revealing tests. We then analytically evaluate and compare our techniques for producing reduced regression test sets

Reducing regression test size by exclusion.

Operational software is constantly evolving. Regression testing is used to identify the unintended consequences of evolutionary changes. As most changes affect only a small proportion of the system, the challenge is to ensure that the regression test set is both safe (all relevant tests are used) and unclusive (only relevant tests are used). Previous approaches to reducing test sets struggle to find safe and inclusive tests by looking only at the changed code. We use decomposition program slicing to safely reduce the size of regression test sets by identifying those parts of a system that could not have been affected by a change; this information will then direct the selection of regression tests by eliminating tests that are not relevant to the change. The technique properly accounts for additions and deletions of code. We extend and use Rothermel and Harrold’s framework for measuring the safety of regression test sets and introduce new safety and precision measures that do not require a priori knowledge of the exact number of modification-revealing tests. We then analytically evaluate and compare our techniques for producing reduced regression test sets

Nonparametric and semiparametric group testing regression models

This dissertation consists of three projects in the area of group testing. The method of group testing, through the use of pooling, has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest, such as infection status. The salient feature of group testing that provides for these gains in efficiency is that testing is performed on pooled specimens, rather than testing specimens one-by-one. In Chapter 1, we present a general introduction of group testing. Typically, the statistical literature surrounding group testing has investigated the implementation of pooled testing for the purposes of either case identification or estimation. In this dissertation, we mainly focuses on the estimation problem which involves the development of regression models that relate individual level covariates to testing responses observed from pooled specimens. Primarily, the existing research in the area of estimation in group testing has focused on parametric regression models, where the shape of the link function is assumed as known and only a finite number of regression parameters has to be estimated. Recently, for the purpose of obviating the specification of the link function and increasing the flexibility of modeling, nonparametric group testing regression models have been studied. %It considers the case where each individual has one continuous explanatory variable and the link function is a univariate probability curve. Existing methods of estimating this unknown function are based on local moment estimators. In Chapter 2, we propose a new nonparametric estimation procedure using a local likelihood approach. For easy illustration, in this part we consider the situation where each individual is assigned to exactly one pool and only this pooled specimen is tested. Further, we assume the assay used for screening is perfect. Both of these two assumptions will be relaxed in the rest chapters of this dissertation. We show that our proposed estimator enjoys an asymptotic normal distribution with the optimal nonparametric estimation rate. Finite sample performance of the method is exhibited via some simulated examples and a real data analysis. To pursue a more suitable technique of modeling group testing data, in Chapter 3, we develop a general semiparametric framework which allows for the inclusion of only not one continuous covariate, but also multiple explanatory variables, all variants of decoding information, and imperfect testing. The asymptotic properties of our estimators are presented and guidance on finite sample implementation is provided. We illustrate the performance of our methods through simulation and by applying them to chlamydia and gonorrhea data collected by the Nebraska Public Health Laboratory as a part of the Infertility Prevention Project. In Chapter 4, we focus on the evaluation of misclassification effect of testing pools which are constructed according to any types of group testing algorithms. The existing assumption regarding them are somehow restrictive. If they are invalid, the estimation procedure can lead to severely biased estimator. In this work, we relax previously made assumptions regarding testing error rates by acknowledging the underlying mechanistic structure of the diagnostic test being employed. For easy illustration of this methodology, we mainly concentrate in parametric regression methods and propose a general estimation framework that allows for the analysis of data arising from all group testing strategies. The finite sample performance of our proposed methodology are investigated through simulation and by applying our techniques to hepatitis B data from a study involving Irish prisoners. Through these studies, we show that our methods can result in more efficient parameter estimates, when compared to competing procedures that make use of individual level data, at a fraction of the cost of data collection. Before proceeding to the main body of this dissertation, I would like to clarify that the notations defined in this work are self-contained in each separated chapter