Data Flow Testing Research Articles

Recent trends on data flow testing : A review

The research aims to comprehensively examine the current state of knowledge in data flow testing (DFT) to identify knowledge gaps and inform future research. The authors undertook this research question to advance the practice of DFT and improve software systems’ quality and reliability. The authors analyze several state-of-the-art techniques, including the correlation tree concept and particle swarm optimization algorithm, leveraging the data flow knowledge during test execution, as well as neural networks and genetic algorithms. The authors also discuss the methods used to evaluate the effectiveness and accuracy of the various techniques, including case studies, simulations, and test data-generating techniques. The authors aim to provide a superficial understanding of the field’s current state and note that future work could focus on in-depth analysis of specific areas within DFT. Future researchers can use this research to gain a deeper understanding of current algorithms and work on improving them. This is particularly important as DFT is an essential part of the testing process for any software.

Deep neural network supervision and data flow testing

Deep neural network supervision and data flow testing

Empirical Comparison of Higher-Order Mutation Testing and Data-Flow Testing of C# with the Aid of Genetic Algorithm

Data-Flow and Higher-Order Mutation are white-box testing techniques. To our knowledge, no work has been proposed to compare data flow and Higher-Order Mutation. This paper compares all def-uses Data-Flow and second-order mutation criteria. The comparison will support the testing decision-making, especially when choosing a suitable criterion. This compassion investigates the subsumption relation between these two criteria and evaluates the effectiveness of test data developed for each. To compare the two criteria, a set of test data satisfying each criterion is generated using genetic algorithms; the set is then used to explore whether one criterion subsumes the other criterion and assess the effectiveness of the test set that was developed for one methodology in terms of the other. The results showed that the mean mutation coverage ratio of the all du-pairs adequate test cover is 80.9%, and the mean data flow coverage ratio of the second-order mutant adequate test cover is 98.7%. Consequently, second-order mutation “ProbSubsumes” the all du-pairs data flow. The failure detection efficiency of the mutation (98%) is significantly better than the failure detection efficiency of data flow (86%). Consequently, second-order mutation testing is “ProbBetter” than all du-pairs data flow testing. In contrast, the size of the test suite of second-order mutation is more significant than the size of the test suite of all du-pairs.

Guided Intelligent Hyper-Heuristic Algorithm for Critical Software Application Testing Satisfying Multiple Coverage Criteria

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert’s decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods.

Nomex Material and Nomex Honeycomb Structure Honey Comb Structure in Electre Method

This paper presents detailed experimental results. Paper A primer on honeycomb mechanical modelling architectures Studies always focus on stable conditions paying; Some random innovative honeycomb Based paper assembly structures their They have excellent mechanical performance and are specific Significant in recent years due to operations have received attention. The geometrically varying feature of honeycomb structures is the hollow cells that form between the thin vertical walls. The hexagonal shape of the actual honeycomb is usually the strongest shape. Advantages of Honeycomb Mixtures Exceptional strength to weight ratio. High hardness fire and fungus resistant honeycomb structures: It is found in soils ranging from 0.02 mm to 0.002 mm. ELECTRE (Elimination and Choux Traduisant La Reality - Elimination and Choice expresses reality) methods from recruitment to many real-world decision-making there are problems widely used. Transportation and more. Theoretical research on the fundamentals of electret methods is also active at this time. Alternative: Length of structure, Width of structure, Total thickness of structure, Hexagonal core height, Upper and lower sheet, longitudinal tension strength. Evaluation Preference: Population size, Number of generation, Crossover probability, Crossover distribution index. From the result it is seen that data flow testing is got the first rank where as is the Crossover distribution index is having the lowest rank.

Data Flow Testing and Tools Review

Data Flow Testing and Tools Review

E052 Implementation of a disease modifying anti-rheumatic drug blood monitoring software: 8 years of experience in a single centre

Abstract Background/Aims Who is responsible for the blood monitoring of disease modifying anti-rheumatic drugs (DMARDs)? It may be Primary Care, Specialist Rheumatology teams or a combination. In Greater Manchester, it was deemed good practice to initiate ‘amber’ conventional synthetic DMARDs (cs-DMARDs) by Rheumatology and then transfer to Primary Care under shared care after 3 months or when blood monitoring was stable. ‘Red’ drug prescribing and monitoring for targeted synthetic DMARDs (ts-DMARDs) and biologics (b-DMARDs) would remain under Specialist care long-term. In 2013, the process began for the Rheumatology team at Bolton to take on the ‘in house’ initiation of cs-DMARDs and the implementation of formal shared care. It would not be possible to manage this with paper slips of pathology results and shared care booklets, as the workload and safety implications would be un-manageable for our small team. The Rheumatology department purchased blood monitoring software, allowing only abnormal results or concerning trends to filter through. The implementation of this new way of working required collaborative working with the Informatics, Laboratory, Software and Rheumatology teams, as well as Primary care. At the time, the likely numbers of patients monitored and the workload of abnormal results/trends was unknown. Here we share our experience of software-based rheumatology DMARD monitoring in a district general hospital setting, serving a population of nearly 300 000. Methods Roles and responsibilities were allocated across the multidisciplinary, administration/clerical and informatics teams. A workflow and weekly timetable of activity and cross cover for leave was implemented. Two ‘super-users’ (AP/LB) undertook the initial testing of the software with informatics support (DE) and still work closely to address any data flow issues, software updates and testing related to emerging DMARDs. The patient cohort included those starting ‘amber’ DMARDs (to the point of shared care), stabilization following a dose increase and those remaining on specialist only ‘red’ DMARDs. Since 2019, a monthly monitoring report has allowed a more detailed documentation of workload. Results There were 518 patient licenses in use December 2019, 589 in 2020 and 635 in 2021. In the pre-pandemic year of 2019, 203 patients were newly registered or re-registered. Abnormal results and concerning trends were ‘flagged’ by the software for 719 patients. 263 shared care agreements were generated for monitored DMARDs. Conclusion This workload data relates to the use of software-based DMARD monitoring in a Rheumatology department, where patient flows relate to 1 whole time equivalent Rheumatology Consultant per 100 000 population. Centres considering taking on a similar form of DMARD monitoring can extrapolate this data for their population and patient flow to guide regarding staffing needs. Disclosure A. Paul: None. L. Bromilow: None. D. Eckersley: None.

On subsumption relationships in data flow testing

SummaryData flow testing creates test requirements as definition‐use (DU) associations, where a definition is a program location that assigns a value to a variable and a use is a location where that value is accessed. Data flow testing is expensive, largely because of the number of test requirements. Luckily, many DU‐associations are redundant in the sense that if one test requirement (e.g. node, edge and DU‐association) is covered, other DU‐associations are guaranteed to also be covered. This relationship is called subsumption. Thus, testers can save resources by only covering DU‐associations that are not subsumed by other testing requirements. Although this has the potential to significantly decrease the cost of data flow testing, there are roadblocks to its application. Finding data flow subsumptions correctly and efficiently has been an elusive goal; the savings provided by data flow subsumptions and the cost to find them need to be assessed; and the fault detection ability of a reduced set of DU‐associations and the advantages of data flow testing over node and edge coverage need to be verified. This paper presents novel solutions to these problems. We present algorithms that correctly find data flow subsumptions and are asymptotically less costly than previous algorithms. We present empirical data that show that data flow subsumption is effective at reducing the number of DU‐associations to be tested and can be found at scale. Furthermore, we found that using reduced DU‐associations decreased the fault detection ability by less than 2%, and data flow testing adds testing value beyond node and edge coverage.

Test-Case Generation for Data Flow Testing of Smart Contracts Based on Improved Genetic Algorithm

Smart contracts are commonly deployed for safety-critical applications, the quality assurance of which has been a vital factor. Test cases are standard means to ensure the correctness of data flows in smart contracts. To more efficiently generate test cases with high coverage, we propose an improved genetic algorithm-based test-case generation approach for smart contract data flow testing. Our approach introduces the theory of particle swarm optimization into the genetic algorithm, which reduces the influence brought by the randomness of genetic operations and enhances its capability to find global optima. A set of 30 real smart contracts deployed on Ethereum and GitHub is collected to perform the experimental study, on which our approach is compared with three baseline approaches. The experimental results show that, in most cases, the coverage of the test cases generated by our approach is significantly higher than the baseline approaches with relatively lower numbers of iterations and lower execution time.

Data flow testing of feature-oriented programs

Data flow testing of feature-oriented programs

Applying genetic algorithm to optimise the software testing efficiency with Euclidean distance

Software testing ensures that the developed software is error free and reliable for customer use. For verification and validation of software products, testing has been applied to these products in various software industries. So, before the delivery of the software to the customer, all the types of testing have been applied. In this paper, automatic test cases have been developed with the help of a genetic algorithm for data flow testing and these tests are divided into different groups using Euclidean distance. Elements of each group are applied to the data flow diagram of the program/software and all the du-paths are found, covering the given test suits. New test suits are generated with the help of the genetic algorithm to cover all du-paths.

Report of Task Group 201 of the American Association of Physicists in Medicine: Quality management of external beam therapy data transfer.

With the advancement of data-intensive technologies, such as image-guided radiation therapy (IGRT) and intensity-modulated radiation therapy (IMRT), the amount and complexity of data to be transferred between clinical subsystems have increased beyond the reach of manual checking. As a result, unintended treatment deviations (e.g., dose errors) may occur if the treatment system is not closely monitored by a comprehensive data transfer quality management program (QM). This report summarizes the findings and recommendations from the task group (TG) on quality assurance (QA) of external beam treatment data transfer (TG-201), with the aim to assist medical physicists in designing their own data transfer QM. As a background, a section of this report describes various models of data flow (distributed data repositories and single data base systems) and general data test characteristics (data integrity, interpretation, and consistency). Recommended tests are suggested based on the collective experience of TG-201 members. These tests are for the acceptance of, commissioning of, and upgrades to subsystems that store and/or modify clinical treatment data. As treatment complexity continues to evolve, we will need to do and know more about ensuring the quality of data transfers. The report concludes with the recommendation to move toward data transfer open standards compatibility and to develop tools that automate data transfer QA.

Automation of Data Flow Class Testing Using Hybrid Evolutionary Algorithms

Background: Software testing is a time consuming and costly process. Recent advances in complexity of software have gained attention among researchers towards the automation of generation of test data. Objective: This paper focuses on the structural testing of object oriented paradigm based software and proposes a hybrid approach to automate the class testing applying heuristic algorithms. Methods: The proposed algorithm performs data flow testing of classes applying all defuses adequacy criteria by automatically generating test cases. A nested 2-step methodology is applied using meta-heuristic genetic algorithm and its two variant (GA-variant1 and Ga-variant2) to produce optimized method sequences. Results: An experiment is performed applying proposed algorithm on six test classes. The results suggest that proposed approach with GA-variant1 is better than other techniques in terms of Average d-u coverage and Average iterations.

ASSINATURA DO TESTE ESTRUTURAL (ATE) - MÉTRICA BASEADA EM TESTE FLUXO DE DADOS E ANÁLISE DE MUTANTES SIGNATURE OF THE STRUCTURAL TEST (SST) - METRIC BASED ON DATA FLOW TEST AND MUTANT ANALYSIS

The structural test makes it possible to guarantee the quality of the software by analyzing the source code. In this sense, this paper presents a new method of structural testing with the application of data flow testing and mutant analysis techniques. It is an arithmetic model called Signature of the Structural Test (SSt), which receives parameters such as variables, operands, operators and commands, determines a metric with the objective of detecting possible semantic and logical errors between the definitions of variables and their uses, such as also, reducing the test paths in the data graph and the mutants generated. The result was favorable to the continuation of the research with other testsand guidance for the construction of an appropriate tool.

An Automated Tool for Data Flow Testing of ASP.NET Web Applications

An Automated Tool for Data Flow Testing of ASP.NET Web Applications

Classification of Farmer’s Eligibility as Recipients of Subsidized Fertilizer Assistance with C4.5 Algorithm

Farmers are the main actors in agricultural development, have a major contribution in realizing Indonesia’s agricultural vision. To support the realization of agricultural production and productivity targets, the government’s supports is by issuing policies to provide subsidized fertilizer assistance. The criteria for farmers who are eligible to receive subsidized fertilizer assistance are having land less than 1.9 hectares, have been members of farmer groups, and having work as a farmer or a private employee. Data collection was carried out using the method of observation and interviews with farmer groups in Gunung Kidul. Data classification is done by applying the C4.5 algorithm to 300 data of prospective farmers receiving subsidized fertilizer assistance. Application development includes interface design, making data flow diagrams, implementation and testing. From the research that has been done by applying the C4.5 Algorithm for farmer data, there are 300 data with 113 training data. Produce a data classification of farmers who are eligible to receive assistance at 95,58% accurate and targeted to the needs of farmers. This means that the C4.5 algorithm can be used as a classification for determining the eligibility of farmers to receive subsidized fertilizer.

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.

Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

Software testing consumes the major portion of the total efforts required for software development. This activity is very time consuming and labor intensive. It is very hard to do testing in optimal manner. In this paper a new approach is proposed, which uses the nature inspired stochastic algorithm called Cuckoo Search Algorithm (CSA) for the automatic generation of test data for data flow testing. This approach considers all def-use as test adequacy criteria. For assistance to CSA in the state space a new fitness function is also proposed by using the concept of dominator tree and branch distance in a CFG. To validate the proposed approach experiments are carried out on 10 benchmarked programs and findings are contrasted with earlier work done in this domain. Further in order to prove that proposed approach performs better than the above mentioned approaches a statistical difference test (T-test) is also performed.

Automatic data flow class testing based on 2-step heterogeneous process using evolutionary algorithms

Software testing is a systematic process to identify the presence of errors in the developed software performed using test data. Manually generating test data is ineffective in terms of cost, time and code coverage. Past three decades automation of test data generation has been a research problem of interest and a wide range of work has been done to apply evolutionary meta-heuristics. Much work in past is devoted to apply evolutionary algorithms for data flow testing on procedural programs. Generating test cases of testing classes is more challenging. Presented work identifies these challenges and proposes a systematic data flow class testing based on 2-step heterogeneous process using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) evolutionary techniques. A prototype is systematically implemented and explained on an example class. A set of classes are further tested to study efficiency of the proposed work in terms of coverage percentage and execution time.

Artificial bee colony algorithm in data flow testing for optimal test suite generation

Meta-heuristic Artificial Bee Colony Algorithm finds its applications in the optimization of numerical problems. The intelligent searching behaviour of honey bees forms the base of this algorithm. The Artificial Bee Colony Algorithm is responsible for performing a global search along with a local search. One of the major usage areas of Artificial Bee Colony Algorithm is software testing, such as in structural testing and test suite optimization. The implementation of Artificial Bee Colony Algorithm in the field of data flow testing is still unexplored. In data flow testing, the definition-use paths which are not definition-clear paths are the potential trouble spots. The main aim of this paper is to present a simple and novel algorithm by making use of artificial bee colony algorithm in the field of data flow testing to find out and prioritize the definition-use paths which are not definition-clear paths.