Random Testing Techniques Research Articles

A Comparison of Fault Detection Efficiency Between Adaptive Random Testing and Greedy Combinatorial Testing for Control Logics in Nuclear Industrial Distributed Control Systems

Due to the complexity of the nuclear industrial distributed control system (DCS), input-domain testing techniques, including random testing and combinatorial testing, are usually utilized to test the control logics in nuclear industrial DCS. To improve the fault detection efficiency of random testing, the adaptive random testing technique selects a test case that significantly differs from all existing test cases. Similarly, to improve the fault detection efficiency of combinatorial testing, the greedy combinatorial testing technique adopts a greedy strategy to generate test cases that cover more uncovered tuple-combinations of parametric values. In this paper, we designed an experiment to compare the fault detection efficiency between adaptive random testing technique and greedy combinatorial testing technique for control logics of nuclear industrial DCS. Through the analysis of the fault detection ratios, the f-measure values, and the values of average percent of faults detected (APFD) on two experimental subjects, including the commonly used benchmarks in the field of Boolean-specification testing as well as a group of Boolean expressions extracted from the control logics in nuclear industrial DCS, the experimental results give us the following conclusions: (1) If the test suites' sizes are relatively small, the fault detection efficiencies of the two techniques are very close though there is a slight advantage in adaptive random testing; (2) With the gradual increase of test suites' sizes, the fault detection efficiency of greedy combinatorial testing is beyond adaptive random testing gradually. Such a result can help us select the appropriate testing techniques in the testing of the control logics in nuclear industry DCS.

THE IMPACT OF WORK ENVIRONMENT, COMMUNICATION AND LEADERSHIP ON PERFORMANCE OF EMPLOYEES IN TARAKAN CITY EDUCATION OFFICE

This study aims to examine and analyze the influence of the working environment of communication and leadership to wards the performance of employees in the education department of Tarakan city. Data collection was done by distributing questionnaires and carried out on 48 people who were samples in this research. The sample technique used is random and data testing techniques used in this research include validity test with factor analysis, reliability test with Alpha Cronbach. Multiple linear regression analysis. The results of the t test showed that the partial variable of the working environment has positive and significant effect on the performance of employees in the education departmet of tarakan city, so the hypothesis in the proposed proved true (T-test = 2,731 > T-table 2.012) .T test results showed that the communication variables have a positive and significant impac on the performance of employees in the education departmet of tarakan city, so the hypothesis in the proposed proved true (T-test = 3,339 > T-table 2.012). T test results showed that the Leadership to word variables have a positive and significant impac on the performance of employees in the education departmet of tarakan city, so the hypothesis in the proposed proved true (T-test = 2,846 > T-table 2.012) The results of f test showed that the working enviroment of comunication and leadership positiv effect and significant to the performance of employees in the education departmet of tarakan city , so the hypothesis in the proposed proved true (F-test = 81,195 > F-tabel 2,82).

THE IMPACT OF WORK ENVIRONMENT, COMMUNICATION AND LEADERSHIP ON PERFORMANCE OF EMPLOYEES IN TARAKAN CITY EDUCATION OFFICE

This study aims to examine and analyze the influence of the working environment of communication and leadership to wards the performance of employees in the education department of Tarakan city. Data collection was done by distributing questionnaires and carried out on 48 people who were samples in this research. The sample technique used is random and data testing techniques used in this research include validity test with factor analysis, reliability test with Alpha Cronbach. Multiple linear regression analysis. The results of the t test showed that the partial variable of the working environment has positive and significant effect on the performance of employees in the education departmet of tarakan city, so the hypothesis in the proposed proved true (T-test = 2,731 > T-table 2.012) .T test results showed that the communication variables have a positive and significant impac on the performance of employees in the education departmet of tarakan city, so the hypothesis in the proposed proved true (T-test = 3,339 > T-table 2.012). T test results showed that the Leadership to word variables have a positive and significant impac on the performance of employees in the education departmet of tarakan city, so the hypothesis in the proposed proved true (T-test = 2,846 > T-table 2.012) The results of f test showed that the working enviroment of comunication and leadership positiv effect and significant to the performance of employees in the education departmet of tarakan city , so the hypothesis in the proposed proved true (F-test = 81,195 > F-tabel 2,82).

Pengaruh Pelatihan dan Motivasi Kerja Terhadap Kinerja Kerja Karyawan PT Asia Multidana di Jakarta

This study was to detect the effect of training and motivation on employee performance studies in the limited partnership. This research was conducted with survey method and performed in 40 employees of PT Asia Multidana. The sampling technique used random sampling and testing techniques of data used in this study include tests of validity by factor analysis, reliability tests with Cronbach alpha, classical assumption test, multiple linear regression analysis, t test to test and prove the hypothesis of the study. The data collected is tested for validity by factor analysis method and tested reliability with an alpha coefficient (Cronbach alpha), which is the result of all the data is valid and reliable. The results of the analysis and discussion show that: (1) training positively affect the performance of employees, (2) positive motivation does not affect the performance of the employee.

Hybrid adaptive random testing

Adaptive random testing (ART) subsumes a family of random testing techniques with an effective improvement. It is based on the observation that failure causing inputs tend to be clustered together. Hence the ART methods spread test cases more evenly within the input domain to improve the fault-detection capability of random testing. There have been several implementations of ART based on different intuitions and principles with their own advantages and disadvantages. In the different variants of ART methods, the majority of them use a variety of distance calculations, with corresponding computational overhead. The newly methods try to decrease computational overhead while maintaining the performance through partitioning the input domain. We outline a new partitioning-based ART algorithm with a hybrid search method and demonstrate experimentally that it can further improve the performance, with considerably lower overhead than other ART algorithms.

An Empirical Comparison of the Efficiency and Effectiveness of Genetic Algorithms and Adaptive Random Techniques in Data-Flow Testing

Software Testing depends on the execution of the tested-program against a set of test-inputs and the comparison of its outputs with the expected ones. The size of the input domain is very large that can be the set of real numbers ( $\mathbb {R}$ ). Thus, the selection of the appropriate inputs is one of the key problems in software testing. This process is time-consuming and needs a lot of effort and budget. Therefore, automatic inputs generation techniques are required to overcome these problems. Genetic algorithms (GAs) have been successfully used for generating test-inputs. Researchers proved that GAs overcame ordinary random search techniques (ORTs) in generating inputs. In addition, GAs can converge faster than ordinary random techniques and they can reduce effectively the size of the test-suite. Unfortunately, technically GAs needs time more than ORTs. Adaptive random testing technique (ART) is a form of ORT that works for distributing test-cases more evenly through the input domain to increase the efficiency of ORTs. So far, there is no study comparing the efficiency of GAs and ARTs in data-flow testing. In this paper, we introduce an empirical comparison for genetic algorithms and adaptive random techniques according to four factors: reducing the size of the test-suite, convergence speed, elapsed time, and the effectiveness in maximizing the coverage ratio of all du-pairs criterion. The experimental study, which was conducted to compare the two techniques, contains 7 Java programs. The results of the experiments showed that the GA technique defeated the ORT technique and the ART technique in reducing the size of the required test-suite to satisfy all du-pairs criterion. Where the GA technique created in total 31532 test-inputs while the ART technique generated 61841 and the ORT technique produced 32064. Further, the results showed that the GA technique converged faster than the ORT technique and the ART technique. Where the procedure of the GA technique was repeated 3153 times totally while the procedure of the ART technique was iterated 6184 times and the procedure of the ORT technique was repeated 3206 times. In addition, the convergence rate of GA-based technique = 8.25 generations/second, the convergence rate of the ORT = 11.98 generations/second, and the convergence rate of the ART = 13.27 generations/second. Moreover, the results showed that the GA technique is faster than the ART technique and slower than the ORT technique. Where the GA technique consumed in total 382 seconds while the ART technique consumed 465.9 seconds and the ORT technique consumed 267.6 seconds. Additionally, the results showed that the GA technique satisfied overall coverage ratio equals 74% of all du-pairs while the ART technique satisfied 78% and the ORT technique satisfied 73%. From these results, we concluded that GA algorithms are more effective than ORT and ART techniques in data-flow testing but ART satisfied the most coverage ratio. Therefore, we recommend hybridizing GA and ART and applying the hybrid technique in the test-data generation process.

Hybrid adaptive random testing

Adaptive random testing (ART) subsumes a family of random testing techniques with an effective improvement. It is based on the observation that failure causing inputs tend to be clustered together. Hence the ART methods spread test cases more evenly within the input domain to improve the fault-detection capability of random testing. There have been several implementations of ART based on different intuitions and principles with their own advantages and disadvantages. In the different variants of ART methods, the majority of them use a variety of distance calculations, with corresponding computational overhead. The newly methods try to decrease computational overhead while maintaining the performance through partitioning the input domain. We outline a new partitioning-based ART algorithm with a hybrid search method and demonstrate experimentally that it can further improve the performance, with considerably lower overhead than other ART algorithms.

Dynamic Random Testing of Web Services: A Methodology and Evaluation

In recent years, service oriented architecture (SOA) has been increasingly adopted to develop distributed applications in the context of the Internet. To develop reliable SOA-based applications, an important issue is how to ensure the quality of web services. In this article, we propose a dynamic random testing (DRT) technique for web services, which is an improvement over the widely-practiced random testing (RT) and partition testing (PT) approaches. We examine key issues when adapting DRT to the context of SOA, including a framework, guidelines for parameter settings, and a prototype for such an adaptation. Empirical studies are reported where DRT is used to test three real-life web services, and mutation analysis is employed to measure the effectiveness. Our experimental results show that, compared with the three baseline techniques, RT, Adaptive Testing (AT) and Random Partition Testing (RPT), DRT demonstrates higher fault-detection effectiveness with a lower test case selection overhead. Furthermore, the theoretical guidelines of parameter setting for DRT are confirmed to be effective. The proposed DRT and the prototype provide an effective and efficient approach for testing web services.

The Effect of Principal Leadership Behaviour and Work Culture Towards Work Discipline Elementary School Teachers on District Minahasa

This This study aims to obtain the results of analysis and clear interpretation of: (1) The positive influence of principals' leadership on the work discipline of elementary school teachers in Minahasa District. (2) The positive influence of work culture on the work discipline of elementary school teachers in Minahasa District. (3) The positive influence of the principal's leadership and work culture simultaneously (together) on the work discipline of elementary school teachers in Minahasa District. Sampling techniques using simple random sampling and data testing techniques used validity test by calculating between scores in each statement with a total score, using the Pearson Product Moment formula and reliability testing using the Cronbach Alpha formula. Testing the analysis requirements used the normality test and linearity test. To prove the research hypothesis on each independent variable with the dependent variable using simple regression analysis. Whereas for the effect of jointly between independent variables and dependent variables using multiple regression analysis. The results of the analysis show that: (1) there is a positive influence on Principal Leadership and Work Discipline, (2) there is a positive influence on the work culture and work discipline, (3) there is a positive influence of Principal Leadership and Work Culture simultaneously (together ) with Work Discipline. Based on the results of the study it can be concluded that the work discipline of elementary school teachers in Minahasa Regency can be improved through improving the quality of the work culture and the function of leadership behavior. Keywords : Principal Leadership Behavior, Work Culture, Work Discipline. DOI : 10.7176/JEP/10-21-02 Publication date :July 31 st 2019

RAMBUTANS: automatic AOP-specific test generation tool

Aspect-oriented programming (AOP) is a programmatic methodology to handle better modularized code by separating crosscutting concerns from the traditional abstraction boundaries. Automated testing, as one of the most demanding needs of the software development to reduce both human effort and costs, is a delicate issue in testing aspect-oriented programs. Prior studies in the automated test generation for aspect-oriented programs have been very limited with respect to the need for both adequate tool support and capability concerning effectiveness and efficiency. This paper describes a new AOP-specific tool for testing aspect-oriented programs, called RAMBUTANS. The RAMBUTANS tool uses a directed random testing technique that is especially well suited for generating tests for aspectual features in AspectJ. The directed random aspect of the tool is parameterized by associating weights to aspects, advice, methods, and classes by controlling object and joint point creations during the test generation process. We present a comprehensive empirical evaluation of our tool against the current AOP test generation approaches on three industrial aspect-oriented projects. The results of the experimental and statistical tests showed that RAMBUTANS tool produces test suites that have higher fault-detection capability and efficiency for AspectJ-like programs.

A Cost-Effective Random Testing Method for Programs with Non-Numeric Inputs

Random testing (RT) has been widely used in the testing of various software and hardware systems. Adaptive random testing (ART) is a family of random testing techniques that aim to enhance the failure-detection effectiveness of RT by spreading random test cases evenly throughout the input domain. ART has been empirically shown to be effective on software with numeric inputs. However, there are two aspects of ART that need to be addressed to render its adoption more widespread—applicability to programs with non-numeric inputs, and the high computation overhead of many ART algorithms. We present a linear-order ART algorithm for software with non-numeric inputs. The key requirement for using ART with non-numeric inputs is an appropriate “distance” measure. We use the concepts of categories and choices from category-partition testing to formulate such a measure. We investigate the failure-detection effectiveness of our technique by performing an empirical study on 14 object programs, using two standard metrics—F-measure and P-measure. Our ART algorithm statistically significantly outperforms RT on 10 of the 14 programs studied, and exhibits performance similar to RT on three of the four remaining programs. The selection overhead of our ART algorithm is close to that of RT.

Testing noninterference, quickly

AbstractInformation-flow control mechanisms are difficult both to design and to prove correct. To reduce the time wasted on doomed proof attempts due to broken definitions, we advocate modern random-testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of increasingly complex information-flow abstract machines, leading up to a sophisticated register machine with a novel and highly permissive flow-sensitive dynamic enforcement mechanism that is sound in the presence of first-class public labels. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important for efficient testing. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for more than 45 bugs. Moreover, we show how testing guides the discovery of the sophisticated invariants needed for the noninterference proof of our most complex machine.

Dynamic Test Reconfigurationfor Composite Web Services

This paper introduces a testing strategy that is suitable for testing service-based applications. We describe an architecture that responds to changes of service operation, operation arguments and service composition changes. Our proof-of-concept test system performs runtime testing on our model atomic and composite web services using a random testing technique. A novel change identification method was developed to capture changes at the service interface. The test system is able to identify changes that occur in service operations and operational arguments in a service description of a test candidate. Our approach uses a new method to detect changes in a service inventory. Automated reconfiguration is used to support the continuous operation of the testing systems during a test candidate change.

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.

A revisit of three studies related to random testing

Software testing is an approach that ensures the quality of software through execution, with a goal being to reveal failures and other problems as quickly as possible. Test case selection is a fundamental issue in software testing, and has generated a large body of research, especially with regards to the effectiveness of random testing (RT), where test cases are randomly selected from the software’s input domain. In this paper, we revisit three of our previous studies. The first study investigated a sufficient condition for partition testing (PT) to outperform RT, and was motivated by various controversial and conflicting results suggesting that sometimes PT performed better than RT, and sometimes the opposite. The second study aimed at enhancing RT itself, and was motivated by the fact that RT continues to be a fundamental and popular testing technique. This second study enhanced RT fault detection effectiveness by making use of the common observation that failure-causing inputs tend to cluster together, and resulted in a new family of RT techniques: adaptive random testing (ART), which is random testing with an even spread of test cases across the input domain. Following the successful use of failure-causing region contiguity insights to develop ART, we conducted a third study on how to make use of other characteristics of failure-causing inputs to develop more effective test case selection strategies. This third study revealed how best to approach testing strategies when certain characteristics of the failure-causing inputs are known, and produced some interesting and important results. In revisiting these three previous studies, we explore their unexpected commonalities, and identify diversity as a key concept underlying their effectiveness. This observation further prompted us to examine whether or not such a concept plays a role in other areas of software testing, and our conclusion is that, yes, diversity appears to be one of the most important concepts in the field of software testing.

Dirt Spot Sweeping Random Strategy

The paper presents a new and improved automated random testing technique named as Dirt Spot Sweeping Random (DSSR) strategy based on the rationale that, when failures lies in the contiguous locations across the input domain, the effectiveness of random testing can be further improved through diversity of test cases. The DSSR strategy selects neighboring values for the subsequent tests on identification of failure. Resultantly, selected values sweep around the failure leading to the discovery of new failures in the vicinity. To evaluate the effectiveness of DSSR strategy a total of 60 classes (35,785 lines of code), each class with 30 x 105 calls, were tested by Random (R), Random+ (R+) and DSSR strategies. T-Test analysis showed significantly better performance of DSSR compared to R strategy in 17 classes and R+ strategy in 9 classes. In the remaining classes all the three strategies performed equally well. Numerically, the DSSR strategy found 43 and 12 more unique failures than R and R+ strategies respectively. This study comprehends that DSSR strategy will have a profound positive impact on the failure- finding ability of R and R+ testing.

Testing noninterference, quickly

Information-flow control mechanisms are difficult to design and labor intensive to prove correct. To reduce the time wasted on proof attempts doomed to fail due to broken definitions, we advocate modern random testing techniques for finding counterexamples during the design process. We show how to use QuickCheck, a property-based random-testing tool, to guide the design of a simple information-flow abstract machine. We find that both sophisticated strategies for generating well-distributed random programs and readily falsifiable formulations of noninterference properties are critically important. We propose several approaches and evaluate their effectiveness on a collection of injected bugs of varying subtlety. We also present an effective technique for shrinking large counterexamples to minimal, easily comprehensible ones. Taken together, our best methods enable us to quickly and automatically generate simple counterexamples for all these bugs.

DEVELOPMENT OF RPS TRIP LOGIC BASED ON PLD TECHNOLOGY

The majority of instrumentation and control (I&C) systems in today's nuclear power plants (NPPs) are based on analog technology. Thus, most existing I&C systems now face obsolescence problems. Existing NPPs have difficulty in repairing and replacing devices and boards during maintenance because manufacturers no longer produce the analog devices and boards used in the implemented I&C systems. Therefore, existing NPPs are replacing the obsolete analog I&C systems with advanced digital systems. New NPPs are also adopting digital I&C systems because the economic efficiencies and usability of the systems are higher than the analog I&C systems. Digital I&C systems are based on two technologies: a microprocessor based system in which software programs manage the required functions and a programmable logic device (PLD) based system in which programmable logic devices, such as field programmable gate arrays, manage the required functions. PLD based systems provide higher levels of performance compared with microprocessor based systems because PLD systems can process the data in parallel while microprocessor based systems process the data sequentially. In this research, a bistable trip logic in a reactor protection system (RPS) was developed using very high speed integrated circuits hardware description language (VHDL), which is a hardware description language used in electronic design to describe the behavior of the digital system. Functional verifications were also performed in order to verify that the bistable trip logic was designed correctly and satisfied the required specifications. For the functional verification, a random testing technique was adopted to generate test inputs for the bistable trip logic.

Diversity oriented test data generation using metaheuristic search techniques

We present a new test data generation technique which uses the concept of diversity of test sets as a basis for the diversity oriented test data generation – DOTG. Using DOTG we translate into an automatic test data generation technique the intuitive belief that increasing the variety, or diversity, of the test data used to test a program can lead to an improvement on the completeness, or quality, of the testing performed. We define the input domain perspective for diversity (DOTG-ID), which considers the distances among the test data in the program input domain to compute a diversity value for test sets. We describe metaheuristics which can be used to automate the generation of test sets for the DOTG-ID testing technique: simulated annealing; a genetic algorithm; and a proposed metaheuristic named simulated repulsion. The effectiveness of DOTG-ID was evaluated by using a Monte Carlo simulation, and also by applying the technique to test simple programs and measuring the data-flow coverage and mutation scores achieved. The standard random testing technique was used as a baseline for these evaluations. Results provide an understanding of the potential gains in terms of testing effectiveness of DOTG-ID over random testing and also reveal testing factors which can make DOTG-ID less effective.

Application of a Failure Driven Test Profile in Random Testing

Random testing techniques have been extensively used in reliability assessment, as well as in debug testing. When used to assess software reliability, random testing selects test cases based on an operational profile; while in the context of debug testing, random testing often uses a uniform distribution. However, generally neither an operational profile nor a uniform distribution is chosen from the perspective of maximizing the effectiveness of failure detection. Adaptive random testing has been proposed to enhance the failure detection capability of random testing by evenly spreading test cases over the whole input domain. In this paper, we propose a new test profile, which is different from both the uniform distribution, and operational profiles. The aim of the new test profile is to maximize the effectiveness of failure detection. We integrate this new test profile with some existing adaptive random testing algorithms, and develop a family of new random testing algorithms. These new algorithms not only distribute test cases more evenly, but also have better failure detection capabilities than the corresponding original adaptive random testing algorithms. As a consequence, they perform better than the pure random testing.