Total Number Of Test Cases Research Articles

Self-piercing riveting process: Prediction of joint characteristics through finite element and neural network modeling

Developing robust numerical simulation tools to investigate the self-piercing riveting process is critical, since the feasibility, quality and strength of riveted connections relies on the successful formation of mechanical interlocks between the rivet and sheet materials. In the present study, we investigate the riveting of aluminum alloy and dual-phase steel sheets (AA7075-F/DP600 and AA6016-T4/DP600) over a wide range of sheet thicknesses, as a function of the rivet and die geometries employed. More specifically, we study the dependence of the probability of successful joint formation, defined as the ratio of the number of acceptable riveted connections to the total number of test cases, on the rivet leg inner radius and die central tip depth. Towards this, we use experimentally calibrated Hosford-Coulomb fracture surfaces for each deformable part, incorporated in dedicated axisymmetric 2D finite element (FE) process models. The FE predictions are validated through comparisons with experimentally obtained riveted joints. Moreover, we analyze the relation between the mechanical interlocks achieved and the rivet and die geometries employed, deriving practice-relevant conclusions with respect to the most favorable design parameters. In particular, we show that while the probability of successful joint formation decreases upon increasing rivet leg inner radius, the joint quality, in terms of effectuated interlock distance per rivet mean residual equivalent plastic strain, increases. Furthermore, we show that machine-learning techniques can be employed to classify with remarkable accuracy the successful joint formation for a wide range of possible self-piercing riveting scenarios, accounting for both rivet and die-related geometrical attributes, as well as for the thickness of the metal sheets.

An Effective Strategy to Build Up a Balanced Test Suite for Spectrum-Based Fault Localization

During past decades, many automated software faults diagnosis techniques including Spectrum-Based Fault Localization (SBFL) have been proposed to improve the efficiency of software debugging activity. In the field of SBFL, suspiciousness calculation is closely related to the number of failed and passed test cases. Studies have shown that the ratio of the number of failed and passed test case has more significant impact on the accuracy of SBFL than the total number of test cases, and a balanced test suite is more beneficial to improving the accuracy of SBFL. Based on theoretical analysis, we proposed an PNF (Passed test cases, Not execute Faulty statement) strategy to reduce test suite and build up a more balanced one for SBFL, which can be used in regression testing. We evaluated the strategy making experiments using the Siemens program and Space program. Experiments indicated that our PNF strategy can be used to construct a new test suite effectively. Compared with the original test suite, the new one has smaller size (average 90% test case was reduced in experiments) and more balanced ratio of failed test cases to passed test cases, while it has the same statement coverage and fault localization accuracy.

Cost-effective test suite minimization in product lines using search techniques

Cost-effective testing of a product in a product line requires obtaining a set of relevant test cases from the entire test suite via test selection and minimization techniques. In this paper, we particularly focus on test minimization for product lines, which identifies and eliminates redundant test cases from test suites in order to reduce the total number of test cases to execute, thereby improving the efficiency of testing. However, such minimization may result in the minimized test suite with low test coverage, low fault revealing capability, low priority test cases, and require more time than the allowed testing budget (e.g., time) as compared to the original test suite. To deal with the above issues, we formulated the minimization problem as a search problem and defined a fitness function considering various optimization objectives based on the above issues. To assess the performance of our fitness function, we conducted an extensive empirical evaluation by investigating the fitness function with three weight-based Genetic Algorithms (GAs) and seven multi-objective search algorithms using an industrial case study and 500 artificial problems inspired from the industrial case study. The results show that Random-Weighted Genetic Algorithm (RWGA) significantly outperforms the other algorithms since RWGA can balance all the objectives together by dynamically updating weights during each generation. Based on the results of our empirical evaluation, we also implemented a tool called TEst Minimization using Search Algorithms (TEMSA) to support test minimization using various search algorithms in the context of product lines.

TFCWS: Testing Framework for composite web services.

In every business domain Web Services are more popular solutions to implement the software. Composite web service can be created by combining basic web services. Many unreliable web services are deployed on the internet. Hence, testing is required to ensure reliability. Software testers have great challenges to test web services. Source code of web services is unavailable. The Testing Framework is used to test web services without knowledge of its internal structure. In this paper, we have proposed a Testing Framework for Composite Web Services (TFCWS). It generates report which shows the total number of test cases executed for each web service with pass or fail status of each test case. It calculates the throughput of web service and response time of each test case. We have used web services response times for analysis of TFCWS, Soap UI and Storm.

Using Mutation to Enhance GUI Testing Coverage

Mutation testing improves a system's bug-detection capability. It also helps improve coverage by exposing software or code areas that other types of testing might not expose. However, the value of mutation testing is often bypassed because it consumes extra resources from already limited ones. The author presents an automatic technique to generate valid and mutant test cases. In traditional mutation testing, one or more parameters in the specification or the code are changed, and the technique finds the test cases that can detect those mutations. In the author's approach, the test cases generated by a GUI model are mutated and the mutants are then applied the model to test its capability to kill the mutant test cases by rejecting them. A robust model is expected to differentiate between a valid and invalid event sequence. The author also describes an automatic execution and verification technique to evaluate the test cases rejected by the GUI model and to calculate coverage based on their number relative to the total number of test cases. Experimental results indicate the value of this mutation process and its potential for improving software test coverage automatically.

Iterative automatic test generation method for telecommunication protocols

Standardized languages used for protocol specification provide an excellent basis for both automatic and manual test generation. Test generation is composed of two steps, the derivation of test cases from the specification, and the selection of the test cases to be included in the final test suite in order to reduce its execution time. This article proposes a new method that aims to decrease the total number of test cases generated automatically by a test derivation algorithm and at the same time to reduce the computation requirements for the test selection procedure. It creates an iteration cycle on the model of evolutionary algorithms, where the test derivation and selection are done simultaneously. In each cycle first a ''small'' test suite is derived then optimized, evaluated and finally compared to the best suite so far. It is kept as the best suite, if it is found better according to some well-defined evaluation criteria and test suite metrics. This iteration condition is based on the test selection criteria. The article presents an experiment where iterative algorithms are compared to two simple test derivation methods from different aspects.

Comparison of Random and Partition Testing Considering the Test Profile

Abstract There has been much interest in the analysis of the effectiveness of partition and random testing. Investigators studied and compared these two strategies from different points of view and came to different conclusions. Some used the probability of detecting at least one failure to quantify the fault-detecting ability of a testing strategy. Some used the expected number of failures detected to assess them. But they did not consider the test profile. Differing from this early work, this paper compares random testing with partition testing by looking at three parameters: the confidence index, the upper confidence bound, and the total number of test cases, considering the test profile. Our results show that random testing generally performs better than partition testing and equally as well as partition testing when the operational profile coincides with the test profile. We also derive the formulas to calculate the three indexes for random and partitional testings.