Test Case Reduction Research Articles

Generalizing the Split Factor of the Minimizing Delta Debugging Algorithm

One of the first attempts at the automation of test case reduction was the minimizing delta debugging algorithm, widely known as ddmin. Despite its age, it is still an unavoidable cornerstone of this field of research. One criticism against ddmin is that it can take too long to reach the granularity where it can perform actual reduction. Therefore, in this paper, ddmin is generalized with respect to the granularity by introducing a new split factor parameter, leading to the formalization of a parametric algorithm variant. The complexity analysis of this parametric variant reveals that the theoretical worst and best-case behavior of ddmin can be improved. Moreover, the results of experiments with the generalized algorithm show that the reduction can be sped up significantly by choosing the right split factor: up to 84% of the test steps can be eliminated in practice.

Test Case Optimization for Enhancing System Software Quality using Genetic Algorithm

Software Product Lines (SPLs) embraces an enormous capacity of feature mixtures which cause challenges for evaluating software programs. Testsuite optimization plays major role to develope the quality of SPLs. In combinatorial testing (CT), pair wise fault coverage maximization and test case reduction accomplishes a substantial role for shrinking the testing cost of software programs. Many research works have been developed and designed for CT using different test suite reduction techniques. However Fuzzy clustering and TSRSO techniques do not provide a finest solution for test suite optimization problem. For that, Genetic Algorithm (GA) Technique is recommended and designed for test suite reduction in CT. Metaheuristic genetic algorithm delivers optimum solution in an effective manner. GA chooses and consolidates the testcases in a testsuite based on some principles such that maximum faults covered with minimum execution time. In Proposed GA, finest individuals are nominated for reproduction in order to create descendants of the succeeding generation. In addition, GA is a superior type of evolutionary algorithms generate finest solutions to optimization problems using selection, crossover and mutation operators. Consequently, GA is applied for resolving test suite reduction problem in CT.

Safety-complete test suites

In this paper, a novel safety-related variant of complete test suites for finite state machines is introduced. Under certain hypotheses which are similar to the ones used in the well-known W-Method and its improved versions, the new method guarantees to uncover every violation of safety properties from a certain well-defined class, while erroneous behaviour without safety relevance may remain undetected. While the method can be based on any of the known complete strategies for FSM testing, its most effective variant is based on the H-method, and this variant is presented in detail, denoted as the Safety-complete H-Method. It is guaranteed that application of the Safety-complete H-Method always results in less or equally many test cases than when applying the original H-Method. In well-defined situations that can be pre-determined from the reference model, the Safety-complete H-Method leads to a substantial reduction of test cases in comparison to the size of the analogous H test suites. We advocate this new test suite for situations, where exhaustive testing of the complete system is too expensive. In these cases, strong guarantees with respect to fault coverage should only be given for the errors representing safety violations, while it may be considered as acceptable if less critical errors remain undetected.

Ant Colony Optimization Based Test Case Selection for Component Based Software

Reusability is one of the prime aspects of high quality software. Based on the concept of reusing the previous effort, Component based software engineering is a widely evolving software development paradigm that sets new challenges for testing team. The third party components need to be selected and assembled in development framework. Components interact with each other for various services and the interface between them can prove as the point of failure. As exhaustive testing of all interaction sequences is not possible, there is need for automated test case reduction and prioritization techniques to increase the efficiency of testing process. Ant Colony Optimization (ACO), a nature inspired optimization technique has wide range of applications in the field of software engineering. This paper presents an ACO based technique for test case selection for interaction testing of reusable software components.

Enhancing test case reduction by k-means algorithm and elbow method

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

An Algorithm for Test Case Reduction in Regression Testing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

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 GA-based test case optimization approaches and provide insight in determining the potential research directions in this area.

Optimization of object-oriented testing using firefly algorithm

Software testing is an integral part of software development. Testing ensures that software is correctly doing what it was designed for and not terminating abruptly. To overcome the challenges associated with exhaustive software testing and to tackle the problems imposed by object-oriented paradigm of software development, a new approach of testing has been used in recent years called model based testing. Model based testing relies on creating test suites from the formal model of application behaviour. Hence, the main aim is to optimize the software process for creation of only effective test cases and omit the redundant ones. Nature-inspired soft computing techniques provide a meta-heuristic approach to achieve the same. In this study, a meta-heuristic firefly algorithm has been proposed for state chart testing of five benchmarks of object-oriented application and optimized test cases have been generated for them. After that, the results have been compared with already existing nature-inspired ant colony optimization algorithm. The inference shows that there is no redundancy in the generated test cases via firefly algorithm whereas test cases generated by ant colony optimization algorithm are highly redundant. Also, firefly algorithm generates reduced test cases as compared to the ant colony algorithm.

Test Case Reduction based upon Path Coverage Criteria

Test Case Reduction based upon Path Coverage Criteria

Using plant model features to generate reduced test cases for programmable controllers

Complete conformance testing is a model-based test technique for programmable controllers. It checks whether an implementation conforms to its specifications with regard to all possible combinations of input signals, which is useful for small scale and safety critical systems. However, the state space explosion issue limits its application to large scale systems. This paper presents a method for reducing state space in generation of test cases by utilizing not only specification models but also features extracted from plant models. The application on a benchmark case study shows that the number of test cases is reduced significantly.

A Framework to Reduce Redundancy in Android Test Suite using Refactoring

Objective: Test cases tend to be large in number as redundant test cases are generated due to the presence of code smells, hence the need to reduce these smells. Methods/Statistical Analysis: This research adopts a proactive approach of reducing test cases by detecting the lazy class code smells based on the cohesion and dependency of the code and applying the inline class refactoring practices before test case generation thereby significantly avoiding redundant test cases from being generated Findings: The test cases generated from the original source code is compared to test cases from the refactored code. Test cases from the refactored code show a reduction of up to 33.3% in cyclomatic complexity compared to the original source code. There was 8.2% improvement in the branch coverage of the generated test cases indicating the efficiency of the refactored code Applications/Improvements: From the results, refactoring is an effective technique that can reduce redundant test cases. While the focus is on test case reduction, it also improves the quality of generated test cases in terms of its branch coverage.

Research of Software Testing Model based on Correlation Defect

Analysis of existing research results of defect testing, software defects during testing should consider the relevance of the defect test software, the design of a Markov chain with the right to test the model, the design of a multi-objective algorithm weights given Markov chain with the right test strategy based on defects associated with the state transition strategy based on multi-objective test weight matrix. The experimental results show that this strategy compared with Controlled Markov Chain testing strategy that can significantly reduce test cases and improve the defect detection rate.

Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

Finding Representative Test Case for Test Case Reduction in Regression Testing

Finding Representative Test Case for Test Case Reduction in Regression Testing

A Hybrid Meta-heuristics Technique for Finding Optimal Path by Software Test Case Reduction

A Hybrid Meta-heuristics Technique for Finding Optimal Path by Software Test Case Reduction

Developing Optimal Directed Random Testing Technique to Reduce Interactive Faults-Systematic Literature and Design Methodology

Background/Objective: The objective is to minimize ambiguous test cases by using Adaptive Genetic Algorithm (AGA). Problem description/Proposed Method: Here we are concerned with the problem of randomly generated test cases. It can contain some ambiguous test cases, which lead to problems at the organizational level. A random algorithm will generate random test cases each time it is run, and it will have resemblance each time. Another problem related to random algorithms is that running them can take a lot of time. To minimize these issues we propose a new technique, which will reduce the given drawbacks. We proposed an Adaptive Genetic Algorithm (AGA), which will provide legal input in each case where it applied. Thus the problem of ambiguity will decrease. Results/Findings: In this research, the near optimal inputs will be generated based on the Adaptive Genetic Algorithm (AGA), which will reduce the illegal inputs. The fault detection rate is used as the fitness function in AGA. To remove the fault proneness, our AGA uses the coverage metrics of the test cases. Conclusion: Random algorithms will generate low cost test cases in large number but problem is that it will consists ambiguous test cases ,to reduce these here we are using AGA which will further reduce test cases by moderating the illegal inputs.

Empirical study of the effects of different profiles on regression test case reduction

Determining how to select a subset of test cases with high-fault detection capability becomes a key issue in code-level regression testing. Cluster analysis has been proposed to deal with this issue. It partitions test cases into clusters based on the similarity of execution profiles. In previous studies, execution profiles were represented as binary or numeric vectors. The vector model only considers the number of times that a function or statement is executed. However, it ignores sequential, the relations and structural information between function calls. Therefore vector-based methods do not always generate satisfying results. In this study, the authors presented cluster analysis of three different types of structural profiles, that is, function execution sequence, function call sequence (FCS) and function call tree. They designed and conducted empirical studies on five medium-sized programs to validate the effects of different profiles on regression test case reduction. Experimental results illustrate that sequential, call relations and structural information can aid to further improve fault detection effectiveness. In view of cost-effectiveness, FCS is regarded as to be the optimal profile. Furthermore, cluster analysis of FCSs is comparable to the additional branch coverage reduction technique with respect to fault detection effectiveness.

Automatic verification technology of software patches for user virtual environments on IaaS cloud

We propose here a technique for automatic verification of software patches for user virtual environments on Infrastructure as a Service (IaaS) Cloud to reduce the cost of verifying patches. IaaS services have been spreading rapidly, and many users can customize virtual machines on IaaS Cloud like their own private servers. However, users must install and verify software patches of the OS or middleware installed on virtual machines by themselves. This task increases the user’s operation costs. Our proposed method replicates user virtual environments, extracts verification test cases for user virtual environments from a test case database (DB), distributes patches to virtual machines in the replicated environments, and executes the test cases automatically on the replicated environments. To reduce test cases creation efforts, we propose an idea of two-tier abstraction which groups software to software groups and function groups and selects test cases belonging to each group. We applied the proposed method on OpenStack using Jenkins and confirmed its feasibility. We evaluated the effectiveness of test case creation efforts and the automatic verification performance of environment replications, test cases extractions, and test case executions.

Using Statistical Usage Testing in Conjunction with Other Black Box Testing Techniques

Cleanroom methodology is a scrupulous incremental software development approach16 for the development of zero defect and high-reliability software using box structure specification, statistical quality control and certification.2 Statistical Usage Testing (SUT) technique is the testing technique defined it Cleanroom software engineering. It based on developing the usage models and then performing statistical tests on the usage models.15 The paper shows the usage of SUT in conjunction with other black box testing techniques. Other types of testing can be performed along with SUT depending on the requirement. Use of other testing techniques can be essential to show specific scenarios of use or to attain full usage model coverage with reduced test cases.17 The paper also presents the effectiveness of applying SUT in conjunction with other black box testing techniques by using various test cases. Other black box testing techniques used include Equivalence Class Partitioning (ECP), Boundary Value Analysis (BVA), Cause Effect Graphing (CEG), Use Case Testing (UCT) and Orthogonal Array Testing (OATS).

An Efficient Test Cases Reduction Approach in User Session Based Testing

An Efficient Test Cases Reduction Approach in User Session Based Testing