Optimal Test Data Research Articles

Implementation Genetic Algorithm for Optimization of Kotlin Software Unit Test Case Generator

Unit testing has a significant role in software development and its impacts depend on the quality of test cases and test data used. To reduce time and effort, unit test generator systems can help automatically generate test cases and test data. However, there is currently no unit test generator for Kotlin programming language even though this language is popularly used for android application developments. In this study, we propose and develop a test generator system that utilizes genetic algorithm (GA) and ANTLR4 parser. GA is used to obtain the most optimal test cases and data for a given Kotlin code. ANTLR4 parser is used to optimize the mutation process in GA so that the mutation process is not totally random. Our model results showed that the average value of code coverage in generated unit tests against instruction coverage is 95.64%, with branch coverage of 76.19% and line coverage of 96.87%. In addition, only two out of eight generated classes produced duplicate test cases with a maximum of one duplication in each class. Therefore, it can be concluded that our optimization with GA on the unit test generator is able to produce unit tests with high code coverage and low duplication.

Maximizing Test Coverage for Security Threats Using Optimal Test Data Generation

As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. It is a constant struggle to maximize test coverage through optimal data generation. We conducted explanatory research to maximize test coverage of security requirements as modeled in the structured misuse case description (SMCD). The acceptance test case is designed through the structured misuse case description for mitigation of security threats. Mal activity is designed from SMCD upon which constraints are specified in object constraint language (OCL) in order to minimize human dependency and improve consistency in the optimal test case design. The study compared two state-of-the-art test coverage maximization approaches through optimal test data generation. It was evident through the results that MC/DC generated optimal test data, i.e., n + 1 test conditions in comparison to the decision coverage approach, i.e., 2n test conditions for security threats. Thus, MC/DC resulted in a significantly lower number of test cases yet maximized test coverage of security threats. We, therefore, conclude that MC/DC maximizes test coverage through optimal test data in comparison to decision coverage at the design level for security threat mitigation.

Automatic generation of test cases for error detection using the extended Imperialist Competitive Algorithm

As computing technology progresses, computer systems and their activity domain are becoming widespread, and software projects are becoming complicated in the current society. Software testing is time-consuming and expensive. It aims at validating software functional and non-functional requirements, including software performance. During the test stages, first, it is specified whether software elements perform their tasks accurately and create correct output. While in software testing at the program code level, we can text all circles and lines of program and conditional parts of the program while there needs data in these tests which can test all these cases and cold pass the program lines with the most coating that is one of the most challenging problems in this type of software tests. Therefore, Imperialist Competitive Algorithm, an advanced algorithm, is considered for producing optimal test data for finding errors in programs. Practical results and evaluating the proposed method with other methods indicate the presented algorithm's excellence.

Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm.

Path testing is the basic approach of white box testing and the main approach to solve it by discovering the particular input data of the searching space to encompass the paths in the software under test. Due to the increasing software complexity, exhaustive testing is impossible and computationally not feasible. The ultimate challenge is to generate suitable test data that maximize the coverage; many approaches have been developed by researchers to accomplish path coverage. The paper suggested a hybrid method (NSA-GA) based on Negative Selection Algorithm (NSA) and Genetic Algorithm (GA) to generate an optimal test data avoiding replication to cover all possible paths. The proposed method modifies the generation of detectors in the generation phase of NSA using GA, as well as, develops a fitness function based on the paths’ prioritization. Different benchmark programs with different data types have been used. The results show that the hybrid method improved the coverage percentage of the programs’ paths, even for complicated paths and its ability to minimize the generated number of test data and enhance the efficiency even with the increased input range of different data types used. This method improves the effectiveness and efficiency of test data generation and maximizes search space area, increasing percentage of path coverage while preventing redundant data.

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FA-DE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm's performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

Particle swarm optimization to produce optimal solution

Search-Based Software Testing is the utilization of a meta-heuristic improving scan procedure for the programmed age of test information. Particle Swarm Optimization (PSO) is one of those technique. It can be used in testing to generate optimal test data solution based on an objective function that utilises branch coverage as criteria. Software under test is given as input to the algorithm. The problem becomes a minimization problem where our aim is to obtain test data with minimum fitness value. This is called the ideal test information for the given programming under test. PSO algorithm is found to outperform most of the optimization techniques by finding least value for fitness function. The algorithm is applied to various software under tests and checked whether it can produce optimal test data. Parameters are tuned so as to obtain better results.

An accelerating PSO algorithm based test data generator for data-flow dependencies using dominance concepts

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like GA, PSO have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an accelerating particle swarm optimization algorithm (APSO) is applied to generate test data for data-flow dependencies of a program guided by a new fitness function. APSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology ERP system were taken for the experimental analysis. The experimental results show that the proposed APSO based approach performed significantly better than random search, genetic algorithm and PSO in enhancing the convergence speed.

Cost and Effectiveness of Search-Based Techniques for Model-Based Testing: An Empirical Analysis

Model-based testing (MBT) seems to be gaining interest in industry and academia due to its provision of systematic, automated and comprehensive testing. The challenge in MBT is to generate optimal test data to execute test cases. Recently, researchers have successfully applied search-based techniques (SBTs) by automating the search for an optimal set of test data at reasonable cost compared to other more expensive techniques. In real complex systems, effectiveness and cost of SBTs for MBT in industrial context are little known. The objective of this study is to empirically evaluate the cost and the effectiveness of SBTs for MBT on industrial case studies. We applied a model-driven approach and SBTs to automatically generate executable feasible test cases. The results show that the model-driven approach generated high number of infeasible test cases with less time while genetic algorithm (GA) and simulating annealing (SA) outperformed significantly random search (RS) with high generation time. We concluded that local SBTs are more appropriate to generate test data when the type of the constraints is simple. Current work on analyzing the cost and effectiveness on SBTs for MBT indicates possible enhancement using the model-driven approach to detect the infeasible paths and SBTs to achieve optimal success rate.

An Adaptive PSO Algorithm Based Test Data Generator for Data-Flow Dependencies using Dominance Concepts

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like genetic algorithm (GA), Particle Swarm Optimization (PSO) have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an Adaptive Particle Swarm Optimization (APSO) algorithm is applied to generate test data for data-flow dependencies of a program guided by a novel fitness function. Adaptive PSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology (KIET), Enterprise resource planning (ERP) system were taken for the experimental analysis. The experimental results show that the proposed adaptive PSO based approach performed significantly better than random search, Genetic Algorithm and PSO in enhancing the convergence speed.

Application of meta-heuristic algorithms for automated software test data generation

The quality of a developed software system depends on factors such as reliability, maintainability, scalability etc. To deliver reliable software, it is necessary that it is defect free. In order to achieve this objective, the software system needs to be thoroughly tested. Since exhaustive testing is not possible, it is necessary to identify suitable test data that helps in improving the quality of a software product. Manual generation of test data is laborious and time consuming due to the presence of a huge number of decision nodes in a program. Meta-heuristic algorithms can help in such scenario by generating optimal test data automatically, from a very large search space of candidate solutions. In this paper, an attempt is made to automatically generate test data using five meta-heuristic algorithms. The proposed approach is applied for a case study bank ATM. Results show that meta-heuristics are efficient in generating optimal test data.

Environment for the analysis of functional self-test quality in digital systems

Dependability of computer architectures has become one of the most important engineering concerns. One of the possibilities to increase the dependability is to develop architectures with dedicated self-test capabilities which allow achieving high quality of testing in terms of fault coverage. We propose a new methodology for Built-in Self-Test (BIST), which combines the inherent functionality of the architecture with a small amount of pre-generated test data stored in the memory, and uses for monitoring of the test process a restricted number of test points, configured as a set of signature analysers. Contrary to the traditional scan-path based logic BIST, the proposed solution does not need additional hardware for test pattern generation, and will not have any impact on the working performance of the system. On the other hand, testing at normal working conditions allows exercising the system on-line and at-speed, facilitating the detection of dynamic faults like delays and crosstalks to achieve high test quality. The new self-test method is free from the negative aspect of over-testing, compared to the traditional logic BIST approaches. A method is presented to generate optimized test data for selected test routines, and to choose minimum set of test- points for response analysis. A tool framework is proposed to emulate self-testing architectures, and to carry out fault simulation for evaluating the test quality in terms of fault coverage.

Cushion Surface Modeling Based on Body Pressure Distribution and Subjective Rating

This paper presents a method of ergonomic design based on subjective rating for the purpose of modeling cushion surface mapped by body pressure distribution (BPD) test data. A sitting comfort evaluation scale was designed to collect subjective comfort perception. Optimal BPD test data were selected by comparing comfort rating after experiments on a trial seat. A data mapping model was established between point clouds in three dimensional coordinate and BPD test data, which can be recognized and transferred in CAD system. In this context, an ergonomic-aided system was developed in practical application to demonstrate the viability of the method. Two designers tried out the system to design a seat cushion, and compared it with conventional method in Rhino software. Results show that the system is more interactive to designers, which can save time of surface modeling by about 50%.

The Features of Testing Critical Program Systems while Their Certification

The problems of creating the procedures and testing scenarios of critical program systems are considered. The obtained procedures allow to generate optimal test data sets, which makes it possible to increase the efficiency of the tests, reduce the laboriousness of license and increase the reliability of its results.

Software Quality Control Based on Genetic Algorithm

Software evaluation has a crucial role in the life cycle of software production system. Producing suitable data for testing the behavior of the software is a subject of many researches in software engineering. In this paper software quality control with criteria of covering application paths is considered and a new method based on genetic algorithm for generating optimal test data is proposed. In this algorithm, the fitness function, population production mechanism and other parameters of genetic algorithm is determined. In addition, the population production stopping criteria is based on critical edges of control flow graph. Critical edges are those that their presence in a control flow graph path represents the presence of other edges and the edges test shows the adequacy of graph test paths. The simulation results on prototype test data show the effectiveness of the proposed method.

Automatic Generation of Regression Test Cases for Web Components using Domain Analysis and Modeling

components and their underlying parts are evolving rapidly to provide services and information over the internet. Web sites are becoming a basic part of business quality of service. As web services play an important role in providing different functionality, their correctness becomes an important factor. The diversity and complexity of web systems derived the need for test automation. Test Automation is the use of software for automatic execution of test cases and their comparison with expected outcome. In this paper, we present an analysis based modeling and regression testing technique for web applications. Model based testing is a test automation approach that generates and maintains more useful and flexible tests from explicit descriptions of the application. Models describing web components structure before and after the changes are incorporated. It helps to identify test cases generated for the initial version of component that can be re- run on the changed component. The regression test-suite generated for the component is optimized using all-path coverage criteria. The proposed approach yields substantial results with optimized test data generation. The test sequences are further optimized using optimization criteria. We start with a survey of related work carried out in testing of web components. There after we provide basic concepts and technical background necessary for regression testing strategy of Web components based on Domain analysis and modeling. A case study to demonstrate testing approach is discussed and results are validated.

The challenges in creating critical-care databases.

As critical care is critical, the demands for patient monitoring are high. The methods need to function properly over a large variety of possible physiological conditions in an environment full of sources for technical artifacts, and they need to support clinical reasoning. Several iterations are usually required in the method development to meet these demands. First, the proposed method is developed and tested with simulated or optimal test data on a proof-of-principle basis. When this level is passed, the method needs to be tested offline with more realistic and nonoptimal data. Only after passing these levels may clinical trials be conducted. Usually, these developments require a significant period of time. Availability of extensive, well-characterized, and well-documented real physiological data during critical care would potentially reduce the amount of time required for the first phases of the method development. Such databases would also enable bench validation of any new methods and objective comparison between different methods. Recently, there have been some attempts to collect large signal databases during critical care. In this article, we aim to discuss the special requirements for collecting signal databases in critical care and to summarize the main features of the existing databases.

Review of respirator performance testing in the workplace: issues and concerns.

Performance capability of respirators has traditionally been evaluated by testing components of the respirator (e.g., filter efficiency), facepiece fit, total inward leakage, or some other measure of performance evaluated under laboratory conditions. In recent years, increased emphasis has been placed on development of test methods suitable for evaluating respirator performance in the workplace. The goal of such testing is to evaluate the level of protection provided by respirators in the work environment. The AIHA Respiratory Protection Committee believes that workplace testing of respirators has the potential to be an excellent tool for increasing knowledge about the effectiveness of respiratory protection. However, a number of technical issues remain to be addressed before optimal test protocols and data analysis methods can be defined. The progress made to date in workplace testing will be reviewed, and broader discussion about key elements that must be considered when developing guidelines for testing respirators in the workplace will be initiated.