Test case reduction methods by using CBR

Journal of Software, 2016

The main purpose of test case reduction is to decrease the number of test cases in order to minimize the time and cost of executing them. We used the data mining approach, mainly because of its ability to extract patterns of test cases that are invisible. In this paper, we use two data mining classifiers, Naï ve based and J48, in order to classify a set of test cases and identify which test case is redundant or irredundant. The results show the applicability of data mining classification in removing the number of redundant test cases.

Software development is a planned and structured process includes research, new development, modification, re-engineering results in software products. The structure of development of software is SDLC. It contains various process and activities to accomplish the task of the software product. In SDLC, testing is the most expensive phase. It is used to validate the software with all possible combinations. Exhaustive testing is impossible task in software testing, as, it is impractical with large software products. Now, automation is applied to generate the test cases for various applications, but the selection and reduction of test cases by automation is the main problem. This target only be achieved, when test cases is contained in large test suites. The technique we propose for test case reduction, called TestExude. It is a technique which gives test cases needed and discard other test cases from the Test Suite. It reduces the redundant test cases and its corresponding elements. This technique works on frequency of test cases in test suite and accepts largest frequency occurred in test suites and discard test cases on the base of frequency selected. Storage is well managed in this technique by reducing the test cases drastically in test suite. Test Suites size, user time, organization cost, storage cost are reduce efficiently. Ultimately, it improves and generate effective test case. This technique is more advantageous than any other techniques as it reduces the cost and time to its minimum extent.

Software test cases play an important role in Software Testing. A lot of test cases are developed while designing test cases that are of no use or redundant in nature. These types of test cases increase the effort and cost involved in testing and results in decrease of software testing efficiency. In this paper, a Genetic Algorithm based methodology has been proposed which can significantly reduce the test suite and leads to optimization of the efficiency of software testing. The testing efficiency can be increased by identifying the most critical paths. The proposed Genetic Algorithm based approach select the software path clusters that are weighted in accordance with the criticality of the path. Proposed algorithm has been evaluated by applying simple Genetic Algorithm and Fitness Scaling on a case study. GA with Fitness scaling helps in improving the efficiency of software testing by effectively reducing software test cases.

Software testing is an important but expensive phase of software development life cycle. During software testing and retesting, development organizations always desire to validate the software from different views. But exhaustive testing requires program execution with all possible combinations of values for program variables, which is impractical due to resource constraints. For many applications, it is possible to generate test cases automatically. But the core problem is the selection of effective test cases necessary to validate the program during the maintenance phase. This target can only be achieved by eliminating all the redundant test cases from the generated pool of test suites. In this paper, we propose a novel test case reduction technique called TestFilter that uses the statement-coverage criterion for reduction of test cases. To demonstrate the applicability of this approach, we conduct an experimental study. The results show that our technique is beneficial in identifying non-redundant test cases at a little cost. Ultimately it is beneficial to optimize time & cost spent on testing and it is also helpful during regression testing.

Test suite reduction is a critical activity which occurs pre or post test cases generation process. As software keeps growing large amounts of new test cases will be generated and added to the test pool and others will be updated. Accordingly test suite size will increase. Test suite reduction techniques have been pr oposed to eliminate redundant or irrelevant test cases based on variant criteria, while seeking to maintain the total effectiveness of the reduced test suite. In this paper we address and compare four test case reduction techniques: CBR (case-based reasoning) deletion algorithms Technique, GE & GRE Heuristics and priority cost technique, Model-checker based technique and Base choice coverage criterion technique. The aim of the study is to provide a guideline for choosing the appropriate test suite reduction techniques.

Computers, Materials & Continua

Regression testing (RT) is an essential but an expensive activity in software development. RT confirms that new faults/errors will not have occurred in the modified program. RT efficiency can be improved through an effective technique of selected only modified test cases that appropriate to the modifications within the given time frame. Earlier, several test case selection approaches have been introduced, but either these techniques were not sufficient according to the requirements of software tester experts or they are ineffective and cannot be used for available test suite specifications and architecture. To address these limitations, we recommend an improved and efficient test case selection (TCS) algorithm for RT. Our proposed technique decreases the execution time and redundancy of the duplicate test cases (TC) and detects only modified changes that appropriate to the modifications in test cases. To reduce execution time for TCS, evaluation results of our proposed approach are established on fault detection, redundancy and already executed test case. Results indicate that proposed technique decreases the inclusive testing time of TCS to execute modified test cases by, on average related to a method of Hybrid Whale Algorithm (HWOA), which is a progressive TCS approach in regression testing for a single product.

2006

The primary purpose of Software Testing Process and Evaluation (STP& E) is to reduce risk. All testing provides insight and helps identify "unknown-unknowns". This paper describes STP with Assured Confidence techniques. The uncertainty problem is an important issue in the computer industry today and testing is still the main technique for quality assurance. There is a need to ensure that the software is reasonably safe from severe faults after testing. When faced with financial or a schedule constraints, testing is usually cut horizontally attempting to cover as many different test requirements at the expense of depth. We have reached a point where we must test smarter and apply Statistical-Risk-Based Test with Assured Confidence (SRBTAC) management procedure. We need to pick the right assessment tools to make vertical cuts in our test strategies. Any given test has a low probability of detecting a problem, but all the possibilities create a too-high probability of defects...

Abstract� This paper presents a strategy for test case size reduction that is applicable to model-based testing approaches. The strategy is based on grouping test cases according to common preconditions and ordering them according to a similar degree of steps. A group of steps that is a prefix of a previous test case is eliminated from the subsequent ones to avoid having to execute and check them again. The prefix then may become a precondition that can either be quickly followed or replaced by a shortcut during test execution. A case study is presented focusing on manual test execution to illustrate the strategy and potential benefits that can be gained.

International Journal of Computer Applications, 2017

The cost of commercial software systems is usually over budget and within limited time duration. Most critical and time consuming phase of a software development process is effort estimation and 50% of this phase is often devoted to testing effort estimation. If we can find a way to reduce effort estimation, then it will greatly deduct total cost and effort needed to be spent in software development. Researchers have been continuously trying to find new ways to reduce effort estimation and improving methods which have been devised already. This paper proposes to use path-oriented test case generation and their reduction with reference to different priorities such as complexity, impact, path coverage etc. We are using different examples to reduce the test cases using various factors i.e. Test case Complexity, its impact and the path coverage.

International Journal of Computer Applications, 2014

Software testing is very labor intensive task for developing software and improving its quality. According to some researchers and software professionals,50% of the time, cost and effort are spent on software testing.To test software, generating test cases is the most important task.Testing can be done either manually or automatically by using various testing tools. In today's scenario software are testedautomatically with the help of tools as it is a fast and accurate process of testing software. Although various testing tools are available in market and are used by testers to test the software and to generate test cases and test data automatically.There are various techniques available for generating test cases like fuzzy logic, finite state machine, neural networks, genetic algorithms, soft computing, genetic programming, evolutionary computation and many others.This paper presents various test case generation methods, test case minimization, selection, and prioritization and evaluation techniques.This paper also focuses on various test case prioritization and selection techniques that help the test engineers to schedule and rank the test cases to reduce the total effort, time and the cost.