Test Data Generation Research Articles

Model-free information-theoretic approach to infer leadership in pairs of zebrafish.

Collective behavior affords several advantages to fish in avoiding predators, foraging, mating, and swimming. Although fish schools have been traditionally considered egalitarian superorganisms, a number of empirical observations suggest the emergence of leadership in gregarious groups. Detecting and classifying leader-follower relationships is central to elucidate the behavioral and physiological causes of leadership and understand its consequences. Here, we demonstrate an information-theoretic approach to infer leadership from positional data of fish swimming. In this framework, we measure social interactions between fish pairs through the mathematical construct of transfer entropy, which quantifies the predictive power of a time series to anticipate another, possibly coupled, time series. We focus on the zebrafish model organism, which is rapidly emerging as a species of choice in preclinical research for its genetic similarity to humans and reduced neurobiological complexity with respect to mammals. To overcome experimental confounds and generate test data sets on which we can thoroughly assess our approach, we adapt and calibrate a data-driven stochastic model of zebrafish motion for the simulation of a coupled dynamical system of zebrafish pairs. In this synthetic data set, the extent and direction of the coupling between the fish are systematically varied across a wide parameter range to demonstrate the accuracy and reliability of transfer entropy in inferring leadership. Our approach is expected to aid in the analysis of collective behavior, providing a data-driven perspective to understand social interactions.

메트릭에 따른 탐색 기반 테스팅 알고리즘 비교

Search-Based Software Testing (SBST) is an effective technique for test data generation on large domain size. Although the performance of SBST seems to be affected by the structural characteristics of Software Under Test (SUT), studies for the comparison of SBST techniques considering structural characteristics are rare. In addition to the comparison study for SBST, we analyzed the best algorithm with different structural characteristics of SUT. For the generalization of experimental results, we automatically generated 19,800 SUTs by combining four metrics, which are expected to affect the performance of SBST. According to the experiment results, Genetic algorithm showed the best performance for SUTs with high complexity and test data evaluation with count . On the other hand, the genetic simulated annealing and the simulated annealing showed relatively better performance for SUTs with high complexity and test data evaluation with count . Genetic simulated annealing, simulated annealing and hill climbing showed better performance for SUTs with low complexity.

Models for predicting elastic modulus and tensile strength of carbon, basalt and hybrid carbon-basalt FRP laminates at elevated temperatures

Basalt fiber-reinforced polymers (B) composite laminates have been extensively used over the last decade in externally strengthening of reinforced concrete (RC) slabs and beams in flexure and shear. Basalt fibers have higher thermal resistance, corrosion resistance, and ductility than the commonly used carbon (C) laminates. However, there is a lack of knowledge about the mechanical properties of such laminates and their hybrid combinations when exposed to elevated temperatures. This paper presents the results of an experimental program that studies the mechanical properties of carbon (C), basalt (B), and their hybrid combinations (BC, CBC, CCB, BBC, and BCB) of multiple layers at elevated temperatures. The experimental program consists of 140 coupon specimens were prepared and tested after being exposed to different temperatures ranging from 25 to 250°C. The results showed that both the elastic modulus and the tensile strength of the C and B laminates degraded with the increase in temperature. However, the degradation was greater in the C composite sheets. Based on the experimental results, it was also observed that the mechanical degradation was the highest in C laminates, which reached to almost 90% at 250°C. In addition, the elastic modulus and tensile strength values had shown that the BBC and B laminates had the highest mechanical performance when exposed to elevated temperatures. In addition, analytical models are proposed from the generated test data to predict the variation in the elastic modulus and tensile strength with temperature. The obtained results and proposed models can be used as input parameters in the analysis and design of externally strengthened members with such FRP laminates. This study strongly endorses the use of B and hybrid combination of B and C laminates in strengthening RC slabs and beams.

An Efficient Test Data Generation Approach for Unit Testing

To ensure the delivery of high-quality software, software testing plays the vital role. One of the major time-consuming and expensive activities in software testing is the generation of test data. Test data generation activity has a strong impact on the effectiveness and efficiency of the whole testing process. In order to reduce the cost and time involved in the process of test data generation, researchers and practitioners have tried to automate it. In literature, many such techniques have been developed and the most commonly used are; Random testing, Symbolic execution and evolutionary testing. In this work, an enhanced and efficient Random test data generation approach is proposed and investigated on a suite of programs and its efficiency is compared with the Genetic algorithm which is an evolutionary approach. The inconsistency of random approach is that it is not capable of generating a specific set or combination of test cases for the program input variables and in search of these effective test cases multiple populations needs to be created that will increase the burden of size . So, in order to remove these inconsistencies from the test suite, it is seeded with a more effective set of test cases through our proposed approach in order to make test suite more granular and limit its size by not generating more populations in search of these effective test cases. In addition to the proposed approach, the classification of test adequacy criteria and issues with random, symbolic execution and genetic algorithm based test data

Auditing buffer overflow vulnerabilities using hybrid static–dynamic analysis

Buffer overflow (BOF) vulnerabilities when present in code can be exploited to violate security objectives such as availability, confidentiality and integrity. They make up substantial portion of input manipulation attacks due to their common presence and ease of exploitation. In this study, the authors propose a hybrid approach combining static and dynamic program analysis with machine learning to audit BOFs. Simple rules to generate test data is proposed to confirm some of the vulnerabilities through dynamic analysis. Confirmed cases can be fixed by developers without further verification. Statements whose vulnerability is not confirmed by dynamic analysis are predicted by mining static code attributes. In the authors’ evaluation using standard benchmarks, their best classifier achieved a recall over 93% and accuracy >94%. Dynamic analysis itself confirmed 34% of known vulnerabilities along with reporting six new bugs, thereby reducing by third, otherwise needed manual auditing effort.

An empirical study to quantify the characteristics of Java programs that may influence symbolic execution from a unit testing perspective

In software testing, a program is executed in hopes of revealing faults. Over the years, specific testing criteria have been proposed to help testers to devise test cases that cover the most relevant faulty scenarios. Symbolic execution has been used as an effective way of automatically generating test data that meet those criteria. Although this technique has been used for over three decades, several challenges remain and there is a lack of research on how often they appear in real-world applications. In this paper, we analyzed two samples of open source Java projects in order to understand the characteristics that may hinder the generation of unit test data using symbolic execution. The first sample, named SF100, is a third party corpus of classes obtained from 100 projects hosted by SourceForge. The second sample, called R47, is a set of 47 well-known and mature projects we selected from different repositories. Both samples are compared with respect to four dimensions that influence symbolic execution: path explosion, constraint complexity, dependency, and exception-dependent paths. The results provide valuable insight into how researchers and practitioners can tailor symbolic execution techniques and tools to better suit the needs of different Java applications.

A Formal Model of Robustness Testing for an Object-Oriented Specification

The work presented in this paper proposes a formal model of constraints for testing the conformity of an implementation from its specification. The principal idea of our approach is based on an equivalence partitioning of input domains for each method type in an object oriented (OO) paradigm for detecting the different classes of errors. The main contribution of our approach is the use of invalid data which do not satisfy the precondition constraints for testing the robustness of entities in an OO model. Indeed, the first objective of the proposed work is to develop a theoretical model of constraint in order to test the conformity of classes. The second objective of our approach is to detect anomalies in invalid input data which induce valid output constraints. The implementation of this approach is based on a random generation of test data and analysis by formal proof.

Test Cases Generation on Robotics for basis Path Testing using Genetic Algorithm

The paper explores the Genetic Algorithm approach to generate adequate and accurate test data for a specific target path. Software plays an important role in many of the systems, where the usage of software for a variety of purposes in different domains of modern life is rapidly increasing. With advancements in technology, it becomes quite complex whereas, software often contains errors. So testing consumes more money and time, which leads to automation that reduces human effort in finding bugs and errors. Actually, Automation in the last phase of system development is similar to manual testing. In both cases, bugs are detected only after code has been complete. Software testing is the most important component of software development process. Path testing is a popular structural testing method which uses the source code of a program to find every possible executable path. Test data generation is a key problem in software testing and its automation will improve the efficiency and effectiveness of software testing. Genetic Algorithm is an adaptive heuristic search algorithm that premise on evolutionary ideas of natural selection and genetic. In this paper, Genetic Algorithm is used to generate path by converting the program into its corresponding Control Flow Graph and then automatically generates the test data for the target path using different sets of GA operators.

A systematic review on search based mutation testing

ContextSearch Based Software Testing refers to the use of meta-heuristics for the optimization of a task in the context of software testing. Meta-heuristics can solve complex problems in which an optimum solution must be found among a large amount of possibilities. The use of meta-heuristics in testing activities is promising because of the high number of inputs that should be tested. Previous studies on search based software testing have focused on the application of meta-heuristics for the optimization of structural and functional criteria. Recently, some researchers have proposed the use of SBST for mutation testing and explored solutions for the cost of application of this testing criterion. ObjectiveThe objective is to identify how SBST has been explored in the context of mutation testing, how fitness functions are defined and the challenges and research opportunities in the application of meta-heuristic search techniques. MethodA systematic review involving 263 papers published between 1996 and 2014 examined the studies on the use of meta-heuristic search techniques for the optimization of mutation testing. ResultsThe results show meta-heuristic search techniques have been applied for the optimization of test data generation, mutant generation and selection of effective mutation operators. Five meta-heuristic techniques, namely Genetic Algorithm, Ant Colony, Bacteriological Algorithm, Hill Climbing and Simulated Annealing have been used in search based mutation testing. The review addressed different fitness functions used to guide the search. ConclusionSearch based mutation testing is a field of interest, however, some issues remain unexplored. For instance, the use of meta-heuristics for the selection of effective mutation operators was identified in only one study. The results have pointed a range of possibilities for new studies to be developed, i.e., identification of equivalent mutants, experimental studies and application to different domains, such as concurrent programs.

Automated and Optimized Software Test Suite Generation Technique for Structural Testing

Software testing consumes 50% of total software development cost. Test case design gains central importance in testing activity with respect to quality. The manual test suite generation is a time consuming and tedious task which needs automation. Unit testing is normally done in stringent time schedules by the developers or rarely by testers. In structural testing, it is not possible to check exhaustively all possible test data and the quality of test is dependent heavily on the performance of single developer or tester. Thus automation and optimization is required in generating test data to assist developer or tester with the selection of appropriate test data. A novel hybrid technique is developed to automate the test suite generation process for branch coverage criteria using evolutionary testing. The hybrid technique applies both Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to automatically generate test data. This technique improves efficiency and effectiveness of test case generation process when compared to applying Genetic Algorithm or Particle Swarm Optimization alone. The performance of proposed technique is evaluated and is observed that hybrid technique reduces the number of iterations by 47% when compared to GA and PSO applied separately and it reduces the execution time by 52% than GA and 48% than PSO.

Test Data Generation for Cyclic Executives with CBMC and Frama-C: A Case Study

Testers of cyclic executive systems are required to make considerable and repetitive efforts to determine input sequences (tests) for leading a system from a start state to a target state. This task is time-consuming and it might lead testers to produce fewer tests than required; which affects negatively the quality of systems and can reduce time-to-market. We propose an automated test generation approach which integrates two renowned tools: the code slicing plugin of Frama-C and the bounded model-checker CBMC. We also suggest several code metrics to better assess the complexity of code and the effect of code slicing on test derivation with CBMC. The proposed approach has been tested on industrial case studies and significantly reduces test generation effort and computation time on two automotive controllers. The proposed approach alleviates the workload of testers so they are able to focus on producing more tests; thus, increasing the quality of systems.

Exploiting Binary Floating-Point Representations for Constraint Propagation

Floating-point computations are quickly finding their way in the design of safety- and mission-critical systems, despite the fact that designing floating-point algorithms is significantly more difficult than designing integer algorithms. For this reason, verification and validation of floating-point computations are hot research topics. An important verification technique, especially in some industrial sectors, is testing. However, generating test data for floating-point intensive programs proved to be a challenging problem. Existing approaches usually resort to random or search-based test data generation, but without symbolic reasoning it is almost impossible to generate test inputs that execute complex paths controlled by floating-point computations. Moreover, because constraint solvers over the reals or the rationals do not natively support the handling of rounding errors, the need arises for efficient constraint solvers over floating-point domains. In this paper, we present and fully justify improved algorithms for the propagation of arithmetic IEEE 754 binary floating-point constraints. The key point of these algorithms is a generalization of an idea by B. Marre and C. Michel that exploits a property of the representation of floating-point numbers.

A Novel Method for Multiplexed Nanometric Bead Tracking

Single-molecule force spectroscopy is a powerful tool to resolve dynamics and interactions between large bio-molecules. Magnetic Tweezers, for example, probe the mechanics of a biopolymer by tethering it to a micrometer sized bead and tracking the bead using video microscopy and image processing. This technique strongly relies on accurate bead tracking in three dimensions with nanometer resolution. However, it traditionally suffers from extremely low throughput. When parallelizing bead-tracking experiments, the need arises for fast algorithms without compromising accuracy and robustness. Here, we introduce a novel tracking method based on a 3D cross correlation of the bead image with a set of computer-generated reference images. We show that the phase shift of the cross correlation peak is proportional to the bead height. For testing the accuracy, we used Lorentz-Mie scattering theory to generate test data. We found that non-circular effects such as interlacing, small image artifacts and non-uniform illumination do not affect the tracking result. Our algorithm tracks over 100 beads in a 100x100 pixel region-of-interest on a multicore PC in real time. Its speed, accuracy and robustness may improve other techniques that rely on bead tracking such as optical tweezers and acoustic force spectroscopy.

Cloud-based automatic test data generation framework

Designing test cases is one of the most crucial activities in software testing process. Manual test case design might result in inadequate testing outputs due to lack of expertise and/or skill requirements. This article delivers automatic test data generation framework by effectively utilizing soft computing technique with Apache Hadoop MapReduce as the parallelization framework. We have evaluated and analyzed statistically our proposed framework using real world open source libraries. The experimental results conducted on Hadoop cluster with ten nodes are effective and our framework significantly outperforms other existing cloud-based testing models.

Nature-inspired Approaches in Software Faults Identification and Debugging

This paper considers software faults identification along the phases from design to testing and debugging. The following subjects are reviewed and extended: bio-inspired concepts for structuring resilient systems, genetic strategies in test data generation, Ant Colony Optimisation (ACO) algorithms for data flow analyzing and testing, artificial immune systems (AIS) based mutation testing, and fault tolerant approaches inspired by immunity principles in order to increase the software dependability. Data collected during software development cycle can be used to understand the software project evolution and its reliability.

Combination and mutation strategies to support test data generation in the context of autonomous vehicles

Combination and mutation strategies to support test data generation in the context of autonomous vehicles

Combination and mutation strategies to support test data generation in the context of autonomous vehicles

The software used to control autonomous vehicles is a type of embedded system that needs to undergo strenuous testing before deployment. Field testing is the final stage of testing ensuring that autonomous vehicles show the intended behaviour. It usually does not take into consideration the code structure. In this context, a previously proposed testing model and a software tool to support structural testing in the context of autonomous vehicle field testing have been improved to support the generation of new input data from logs collected during field testing using strategies of combination and mutation. We present in this paper three combination strategies and five mutation strategies with the objective of being used in a search-based algorithm for structural data testing generation. A study to assess their coverage according to the criteria all-nodes and all-edges is also shown.

A Comparative Study on Test Case Generation of Concurrent Programs

This paper deals with a comparative study on testing of concurrent programs based on different techniques. The various challenges in testing concurrent programming are: defining test coverage criteria based on control flow, generating control flow graph of nondeterministic programs, investigating the applicability of sequential testing criteria to parallel program testing etc. For solving these issues, some existing techniques are discussed in this study. Various researchers use an intermediate graph called Event Inter Actions Graph (EIAG) to solve the problem of generating the control flow graph of nondeterministic programs. Some researches propose an intermediate graph called Interaction Sequence Testing Criteria (ISTC) approach based on sequence of interactions to solve the problem of test coverage criteria based on control and data flow. Another method to solve the problem of generating test coverage based on control flow graph of nondeterministic programs is constraint based approach. It needs constrained elements to generate test case which includes structural element and constraint. The selection of good test cases has been addressed by test data generation technique. The technique of concurrent path analysis approach is used to solve the problem of applicability of sequential testing criteria to parallel program testing. It reduces the number of combined concurrent test paths. The sequential test paths are combined to form concurrent test path. The Integration and System Test Automation (ISTA) approach is used to solve the problem of applicability of sequential testing criteria to parallel program testing. It is used for automated test case generation and execution by using high-level Petri net is a finite state test model.

Test Data Generator to Improve Code Coverage in Object Oriented Programming

Test Data Generator to Improve Code Coverage in Object Oriented Programming

A GA based Software Test Data to Generator Suitable Test Cases

This work presents a new concept that is applied in genetic algorithm based tester. Genetic algorithm is sort of evolutionary algorithm that uses in searching problem for optimal solution. In presented test input data generation application, the solution sought by genetic algorithm is a set of test data that causes execution of all possible paths of a given program under test. The proposed concept, repetition frequency is a tool to measure high frequented and also dead parts of program. To experiment with proposed genetic generator, random test data generator was implemented too. Both of these generators tested three programs that are benchmark for many researchers in order to show our proposed tester efficiency.