scholarly journals A Strategy for Automatic Quality Signing and Verification Processes for Hardware and Software Testing

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammed I. Younis ◽  
Kamal Z. Zamli
Keyword(s):  
Integrated Circuits ◽  
Production Line ◽  
Test Suite ◽  
Test Cases ◽  
Optimal Test ◽  
Block Partitioning ◽  
Verification Process ◽  
Novel Strategy

We propose a novel strategy to optimize the test suite required for testing both hardware and software in a production line. Here, the strategy is based on two processes: Quality Signing Process and Quality Verification Process, respectively. Unlike earlier work, the proposed strategy is based on integration of black box and white box techniques in order to derive an optimum test suite during the Quality Signing Process. In this case, the generated optimal test suite significantly improves the Quality Verification Process. Considering both processes, the novelty of the proposed strategy is the fact that the optimization and reduction of test suite is performed by selecting only mutant killing test cases from cumulating t-way test cases. As such, the proposed strategy can potentially enhance the quality of product with minimal cost in terms of overall resource usage and time execution. As a case study, this paper describes the step-by-step application of the strategy for testing a 4-bit Magnitude Comparator Integrated Circuits in a production line. Comparatively, our result demonstrates that the proposed strategy outperforms the traditional block partitioning strategy with the mutant score of 100% to 90%, respectively, with the same number of test cases.

scholarly journals How Cognitive Biases Influence the Data Verification of Safety Indicators: A Case Study in Rail

Safety ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 69
Author(s):  
Burggraaf ◽  
Groeneweg ◽  
Sillem ◽  
van Gelder
Keyword(s):  
Decision Making ◽  
Cognitive Biases ◽  
Decision Makers ◽  
Human Cognition ◽  
Quality Data ◽  
Verification Process ◽  
Support Decision Making ◽  
Good Quality Data

The field of safety and incident prevention is becoming more and more data based. Data can help support decision making for a more productive and safer work environment, but only if the data can be, is and should be trusted. Especially with the advance of more data collection of varying quality, checking and judging the data is an increasingly complex task. Within such tasks, cognitive biases are likely to occur, causing analysists to overestimate the quality of the data and safety experts to base their decisions on data of insufficient quality. Cognitive biases describe generic error tendencies of persons, that arise because people tend to automatically rely on their fast information processing and decision making, rather than their slow, more effortful system. This article describes five biases that were identified in the verification of a safety indicator related to train driving. Suggestions are also given on how to formalize the verification process. If decision makers want correct conclusions, safety experts need good quality data. To make sure insufficient quality data is not used for decision making, a solid verification process needs to be put in place that matches the strengths and limits of human cognition.

Optimal Test Case Selection Using Ant Colony and Rough Sets

2020 ◽  
Vol 11 (2) ◽  
pp. 1-14
Author(s):  
Angelin Gladston ◽  
Niranjana Devi N.
Keyword(s):  
Optimization Technique ◽  
Computational Effort ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Optimal Test ◽  
Average Percentage ◽  
Test Case Selection ◽  
Case Selection

Test case selection helps in improving quality of test suites by removing ambiguous, redundant test cases, thereby reducing the cost of software testing. Various works carried out have chosen test cases based on single parameter and optimized the test cases using single objective employing single strategies. In this article, a parameter selection technique is combined with an optimization technique for optimizing the selection of test cases. A two-step approach has been employed. In first step, the fuzzy entropy-based filtration is used for test case fitness evaluation and selection. In second step, the improvised ant colony optimization is employed to select test cases from the previously reduced test suite. The experimental evaluation using coverage parameters namely, average percentage statement coverage and average percentage decision coverage along with suite size reduction, demonstrate that by using this proposed approach, test suite size can be reduced, reducing further the computational effort incurred.

scholarly journals Reliable and sensitive detection of glycosaminoglycan chains with immunoblots

Glycobiology ◽  
2020 ◽  
Author(s):  
Haruna Nagase ◽  
Sayuri L Higashi ◽  
Chinyere A Iweka ◽  
Craig S Pearson ◽  
Yoko Hirata ◽  
...  
Keyword(s):  
Intracellular Signaling ◽  
Science Research ◽  
Biological Processes ◽  
True Positive ◽  
Specific Antibodies ◽  
Complex Glycans ◽  
Novel Strategy ◽  
Convincing Data

Abstract Complex glycans play vital roles in many biological processes, ranging from intracellular signaling and organ development to tumor growth. Glycan expression is routinely assessed by the application of glycan-specific antibodies to cells and tissues. However, glycan-specific antibodies quite often show a large number of bands on immunoblots and it is hard to interpret the data when reliable controls are lacking. This limits the scope of glycobiology studies and poses challenges for replication. We sought to resolve this issue by developing a novel strategy that utilizes an immunoreaction enhancing technology to vastly improve the speed and quality of glycan-based immunoblots. As a representative case study, we used chondroitin sulfate glycosaminoglycan (CS-GAG) chains as the carbohydrate target and a monoclonal antibody, CS-56, as the probe. We discovered that preincubation of the antibody with its antigenic CS-GAG chain distinguishes true-positive signals from false-positive ones. We successfully applied this strategy to 10E4, a monoclonal anti heparan sulfate GAGs (HS-GAGs) antibody, where true-positive signals were confirmed by chemical HS-GAG depolymerization on the membrane. This evidence that glycan-specific antibodies can generate clear and convincing data on immunoblot with highly replicable results opens new opportunities for many facets of life science research in glycobiology.

Study of Test Cases Prioritization Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2168-2172
Author(s):  
Lu Lu Chen ◽  
Ling Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Regression Testing ◽  
Time Limit ◽  
Ant Colony ◽  
Test Suite ◽  
Test Cases ◽  
Optimal Sequence ◽  
Effectiveness And Efficiency ◽  
Important Activity

Regression testing is an important activity to ensure the quality of software. In order to improve the efficiency of regression testing, in this paper, the author proposes to reorder test suite based on ant colony algorithm in regression testing, and compare the result with other common sort results. Through experiment, it shows that the method can get the optimal sequence of test cases under the time limit and it has been proven a superior method in both effectiveness and efficiency for test cases prioritization.

STANDARDIZED WORK IN TPS PRODUCTION LINE

2015 ◽  
Vol 76 (6) ◽  
Author(s):  
Nurul Hayati Abdul Halim ◽  
Ahmed Jaffar ◽  
Noriah Yusof ◽  
Roseelena Jaafar ◽  
Ahmad Naufal Adnan ◽  
...  
Keyword(s):  
Continuous Improvement ◽  
Production System ◽  
Manufacturing Process ◽  
Production Line ◽  
Assembly Line ◽  
Process Stability ◽  
Automotive Assembly ◽  
Study Implementation

This paper presents a case study implementation of one of the Toyota Production System (TPS) tools, known as Standardized Work (SW), in an automotive assembly line in Malaysia. The main functions of SW are to design, develop, document and visualize a set of a manufacturing process with detail and proper study of it. SW is conducted to raise production consistency and quality of a produced product and the job performed. With the proper SW implementation, good results have been obtained from the increase in efficiency, productivity, quality and process stability of the operator’s performance. Thus, the findings are consistent with TPS philosophies which are waste elimination and continuous improvement in any manufacturing area.

Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GA

2021 ◽  
pp. 133-150
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Hybrid Approach ◽  
Regression Testing ◽  
Minimum Time ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Ant Colony Optimization Algorithm ◽  
Test Case Selection

This article describes about the application of search-based techniques in regression testing and compares the performance of various search-based techniques for software testing. Test cases tend to increase exponentially as the software is modified. It is essential to remove redundant test cases from the existing test suite. Regression testing is very costly and must be performed in restricted ways to ensure the validity of the existing software. There exist different methods to improve the quality of test cases in terms of the number of faults covered, opposed to the number of statements covered in a minimum time. Different methods exist for this purpose, such as minimization, test case selection, and test case prioritization. In this article, search-based methods are applied to improve the quality of the test suite in order to select a minimum set of test cases which covers all the statements in a minimum time. The whole approach is named search based regression testing. In this paper, the performance of different metaheuristics for test suite minimization problem is also compared with a hybrid approach of ant colony optimization algorithm and genetic algorithm.

Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GA

2018 ◽  
Vol 9 (3) ◽  
pp. 88-104
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Hybrid Approach ◽  
Regression Testing ◽  
Minimum Time ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Ant Colony Optimization Algorithm ◽  
Test Case Selection ◽  
Case Selection

This article describes about the application of search-based techniques in regression testing and compares the performance of various search-based techniques for software testing. Test cases tend to increase exponentially as the software is modified. It is essential to remove redundant test cases from the existing test suite. Regression testing is very costly and must be performed in restricted ways to ensure the validity of the existing software. There exist different methods to improve the quality of test cases in terms of the number of faults covered, opposed to the number of statements covered in a minimum time. Different methods exist for this purpose, such as minimization, test case selection, and test case prioritization. In this article, search-based methods are applied to improve the quality of the test suite in order to select a minimum set of test cases which covers all the statements in a minimum time. The whole approach is named search based regression testing. In this paper, the performance of different metaheuristics for test suite minimization problem is also compared with a hybrid approach of ant colony optimization algorithm and genetic algorithm.

scholarly journals On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1145 ◽  
Author(s):  
Shweta Rani ◽  
Bharti Suri ◽  
Rinkaj Goyal
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Test Execution ◽  
Mutation Strategy

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

A STUDY ON A PATH-BASED STRATEGY FOR SELECTING BLACK-BOX GENERATED TEST CASES

2001 ◽  
Vol 11 (02) ◽  
pp. 113-138 ◽  
Author(s):  
Y. T. YU ◽  
S. F. TANG ◽  
P. L. POON ◽  
T. Y. CHEN
Keyword(s):  
Classification Tree ◽  
Black Box ◽  
Test Suite ◽  
Test Cases ◽  
Black Box Testing ◽  
Input Domain ◽  
Partition Method ◽  
Tree Method ◽  
Selection Of

Various black-box methods for the generation of test cases have been proposed in the literature. Many of these methods, including the category-partition method and the classification-tree method, follow the approach of partition testing, in which the input domain is partitioned into subdomains according to important aspects of the specification, and test cases are then derived from the subdomains. Though comprehensive in terms of these important aspects, execution of all the test cases so generated may not be feasible under the constraint of tight testing resources. In such circumstances, there is a need to select a smaller subset of test cases from the original test suite for execution. In this paper, we propose the use of white-box information to guide the selection of test cases from the original test suite generated by a black-box testing method. Furthermore, we have developed some techniques and algorithms to facilitate the implementation of our approach, and demonstrated its viability and benefits by means of a case study.

scholarly journals Multiple-Searching Genetic Algorithm for Whole Test Suites

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2011
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Multicast Routing ◽  
Test Suite ◽  
Test Cases ◽  
Coverage Criteria ◽  
Mutation Score ◽  
Problem Instances ◽  
Test Suite Generation ◽  
Test Suites

A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

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