Earticle

Path-oriented test data generation is in essence a Constraint Satisfaction Problem solved by search strategies, among which backtracking algorithms are widely used. In this paper, the backtracking algorithm Branch & Bound is introduced to generate path-oriented test data automatically. A model based on state space search is proposed to construct the search tree dynamically. Aiming at the programs containing constraints of strongly related variables even equalities, the static analysis technique interval arithmetic is optimized for the precise judgment of the assignment to each variable. The analysis on conflict is made accurate via distance for further domain reduction, thus ensuring the precise direction of the next search step. Experiments show that the proposed method outperformed other methods used in static test data generation. Specifically, it produces excellent results when variables are strongly related even when they are in equalities, and generation time increases stably and linearly with the increment of number of expressions including both equalities and inequalities.

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.

This paper presents an approach for extending the constraint model defined for conformity testing of a given method of class to its overriding method in subclass using inheritance principle. The main objective of the proposed work is to find the relationship between the test model of an overriding method and its overridden method using the constraint propagation. Our approach shows that the test cases developed for testing an original method can be used for testing its overriding method in a subclass and then the number of test cases can be reduced considerably. The implementation of this approach is based on a random generation of test data and analysis by formal proof.

Based on the model of matrix, a new algorithm for original pairwise testing data set generation is first proposed, and then, based on which, a sufficient and necessary condition for the minimum pairwise testing data set generation of the testing system T is given, where T has n different parameters, each parameter has n different values, and n is a prime number. Finally, according to the given original pairwise testing data set, a novel testing data adding method based on the idea of matching combination is designed to add extra testing data to cover all the pairwise combinations that are not covered by the original pairwise testing data set. Theoretical analysis and experimental results show that, the newly proposed algorithm is simple and effective, and has these good characteristics such as small test data set and less time consumption etc.

In order to reduce the workload of generating test cases Data-driven testing was introduced. The method was proposed to solve the platform dependency of Record-and-playback. With Data-Driven testing, Testers could focus on generating test data itself so that it contributes to the improvement of productivity. Because a lot of testing data is required for better quality coverage, the workload of testers is explosively increased. In this paper, for efficient generation of test data, we propose the Abstract test script. The abstract test script provides various convenient operators for generating test data. With the Abstract test script, testers can design, implement and manage test data for Data-driven testing efficiently.

This paper presents a technique that based on a combination of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called GPSCA (Genetic-Particle Swarm Combined Algorithm) which is used to generate automatic test data for data flow coverage with using dominance concept between two nodes. The performance of the proposed approach is analyzed on a number of programs having different size and complexity. Finally, the performance of GPSCA is compared to both GA and PSO for generation of automatic test cases to demonstrate its superiority.

Software test data generation is an important part of software testing. This paper presents a multi-agent cooperation framework for software test data generation. The framework is constituted by Graph Miner, Method Selector and test data generation method agent group. Graph Miner extracts software information sequences from UML graphs, and sends them to Method Selector. Then Method Selector selects out relevant method agents to generate test data. This framework can solve the problems of test data generation methods extension and low level of intelligence exist in traditional methods. Based on the framework proposed in this paper, a software prototype is developed. It is proved that this framework is feasible.

Covering as much as 40 to 50 percent of the development costs and resources, software testing is an integral part of the software development lifecycle. Despite its importance, current software testing practice lacks automation and is still primarily based on highly manual processes from the generation of test cases (i.e. from the specifications documents) up to the actual execution of the test. These manually generated tests are sometimes executed using ad hoc approach, typically requiring the construction of a test driver for the particular application under test. The construction of a test driver is tedious, error prone, and cumbersome process, as it puts extra burden to test engineers. In addition, test engineers are also under pressure to test increasing lines of code in order to meet market demands and deadlines for more software functionalities. To attain the required level of quality and maintain acceptable test coverage, test engineers need to consider a significantly large number of test cases. Many combinations of possible input parameters, hardware/software environments, and system conditions need to be tested and verified against for conformance based on the system’s specification. Often, this results into combinatorial explosion of test cases. Addressing some of the aforementioned issues, this paper describes a new automated and combinatorial software testing tool, called JTst, based on the use of Java technology. The main novel features of JTst are the fact that it permits combinatorial test data generation and automated concurrent execution, enabling higher product quality at lower testing costs.

Evolutionary testing is a very popular domain in the field of search based software engineering that consists i n automatically generating test data for a given piece of code using evolutionary algorithms. One of the most important measures used to evaluate the quality of the generated test suites is code coverage. In this paper we first analyze if there exists a correlation between some static measures computed on the test program and the code coverage when an evolutionary test data generator is used. In particular, we use and compare three techniques for the search engine of the test data generator: an Evolutionary Strategy, a Genetic Algorithm, and a Random Search. We have also developed a program generator that is able to create Java programs with the desired values for the given static measures. Our experimental study includes a benchmark of 1800 programs automatically generated. In addition to the correlations study we also analyze the subset of programs for which one algorithm is better than another one. This second analysis could be the basis for the development of a software tool that automatically decides the suitable test data generation search engine according to the static measures computed on the test object.

고수준의 명세나 고수준의 프로그래밍 언어로 작성된 원시 코드를 이용하여 테스트 데이터를 생성하는 것이 일반적이다. 그러나 어떤 상황에서는 이러한 테스트 데이터 생성 정보가 항상 이용가능하지 않을 수 있다. 이 논문에서는 실행가능 목적코드를 바탕으로 테스트 데이터를 생성하는 방법을 제안한다. 제안된 방법은 정교한 목적 코드 분석을 필요로 하지 않은 매우 간단한 함수 최소화 기법을 사용하여 동적으로 테스트 데이터를 생성한다. 삼각형 분류 프로그램에 대한 실험을 통하여 분기 커버리지를 매우 효과적으로 달성함을 보인다.

It is usual for test data generation to be performed using either high-level specifications or source codes written in high-level programming languages. In certain circumstances, however, such information is not always available. This paper presents a technique that generates test data based on executable object codes. The proposed technique makes use of a very simple function minimization technique without sophisticated object code analysis and produces test data dynamically. We have conducted a simple experiment to evaluate the effectiveness of the proposed test data generation technique with a triangle classification program to show that branch coverage can be easily achieved.

As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. This paper proposes a test data generation technique based on adequacy based testing criteria. Adequacy based testing criteria uses the concept of mutation analysis to check the adequacy of test data. In general, mutation analysis is applied after the test data is generated. But, in this work, we propose a technique that applies mutation analysis at the time of test data generation only, rather than applying it after the test data has been generated. This saves significant amount of time (required to generate adequate test cases) as compared to the latter case as the total time in the latter case is the sum of the time to generate test data and the time to apply mutation analysis to the generated test data. We also use genetic algorithms that explore the complete domain of the program to provide near-global optimum solution. In this paper, we first define and explain the proposed technique. Then we validate the proposed technique using ten real time programs. The proposed technique is compared with path testing technique (that use reliability based testing criteria) for these ten programs. The results show that the adequacy based proposed technique is better than the reliability based path testing technique and there is a significant reduce in number of generated test cases and time taken to generate test cases.

Software testing cost can be reduced if the process of testing is automated. However, the test data generation task is still performed mostly by hand although numerous theoretical works have been proposed to automate the process of generating test data and even commercial test data generators appeared on the market. Despite prolific research reports, few attempts have been made to evaluate and characterize those techniques. Therefore, a lot of works have been proposed to automate the process of generating test data. However, there is no overall evaluation and comparison of these techniques. Evaluation and comparison of existing techniques are useful for choosing appropriate approaches for particular applications, and also provide insights into the strengths and weaknesses of current methods. This paper conducts experiments on four representative test data generation techniques and discusses the experimental results. The results of the experiments show that the genetic algorithm (GA)-based test data generation performs the best. However, there are still some weaknesses in the GA-based method. Therefore, we modify the standard GA-based method to cope with these weaknesses. The experiments are carried out to compare the standard GA-based method and two modified versions of the GA-based method.

The COMS IMPS (Communication Ocean and Meteorological Satellite IMage Pre-processing Subsystem) is developed for image pre-processing of COMS. For a test of the COMS IMPS, 7 support software are developed in KARI GS using simulated MI/GOCI WB (Wide-Band) data; COMS Fill Adder, MI (Meteorological Imager) CADU generator, GOCI (Geostationary Ocean Colour Imager) CADU generator, COMS CADU combiner, MI SD (Sensor Data) analyzer, GOCI SD analyzer, and COMS DM (Decomposition Module) test harness. This paper explains functions of developed support software and the COMS IMPS test using those software.

In this paper, we investigated the possibility of using Youden's index, which is a diagnostic test index used in diagnostic medicine, as an interestingness measure among objective thresholds in association rules. First, we looked at the relationship of confidence measures for the sensitivity and specificity that constitute the Youden index. We also examined the relationship between Youden index and confidence measures by expressing the Youden index formula using various types of confidence, and presented the confidence interval of the Youden index. In addition, after grasping the meaningful properties as interestingness measure, we proved whether the condition of interestingness measure proposed by Piatetsky-Shapiro (1991) was satisfied. By simulation study, if the lower limit of the Youden index has a positive value, it is concluded that there is a positive association rule. If the upper limit of the Youden index has a negative value, it is determined that there is a negative association. If the lower limit of the Youden index has a negative value but the upper limit is positive, then it would be desirable to reserve whether or not there is a association rule.

테스트 데이터의 자동 생성은 소프트웨어 테스팅에서 가장 중요하면서도 어려운 부분이다. 대부분의 데스트 데이터 자동생성에 관한 연구는 명세로부터 테스트 데이터를 자동 생성하는 방식이며 이틀 위해 정확한 정형적 명세를 필요로 한다. 본 논문에서 는 프로그램을 실 행하지 않고 프로그램의 동적인 특성을 분석할 수 있는 요약 해석(abstract interpretation) 방법과 선후 지배 관계(pre-, postdominance relationship)를 이용하여 프로그램 코드로부터 직접 테스트 데이터를 자동 생성할 수 있는 방법 을 제안한다.

지속적으로 증가하는 소프트웨어 복잡성으로 인해, 모델 기반 개발 기법은 소프트웨어 개발에 있어 거의 필수적인 기법이 되고 있다. 그러나, 모델 기반 기법을 활용한다 하더라도 복잡한 소프트웨어를 위한 테스트 케이스 생성은 여전히 풀어야 할 숙제이다. 본 논문에서는, 커스텀 파서와 SMT 솔버를 이용해 UML 모델 기반에서 자동 테스트 데이터를 생성하는 기법을 제안한다. 제안된 기법을 이용하면, 모델이 액션 언어(action language)와 같은 플랫폼 독립적인 언어로 구현되어 있거나, 플랫폼 종속적인 언어로 기술되어 있더라도 테스트 입력을 생성할 수 있다. 또한, 모델에서 테스트 케이스를 효율적으로 생성하기 위해 콘콜릭 수행 기법을 적용하였다. 본 논문에서는, 제안된 테스트 데이터 생성 기법을 통해 현대 산타페의 파워윈도우 스위치 모델에 활용된 사례를 기술한다.

Because of the ever-increasing software complexity, model-based development techniques are becoming an essential technique in software development. However, even if model-based techniques are used, the test case generation for complex software is still a challenge to solve. In this paper, we propose a method to generate automatic test cases based on UML model using custom parser and SMT solver. By proposed technique, a test case can be generated even though the model is described in a platform independent language such as action language, or in a platform dependent language. In addition, a concolic execution technique is applied to efficiently generate test cases in the model. In this paper, we present a case study on the power window switch model of Hyundai Santa Fe through the proposed test case generation technique.

최근 다양한 어플리케이션과 전용 임베디드 시스템의 개발 필요성이 증가하고 있으며 이는 Smart Community를 추구하는 디지털 지식사회의 중요한 이슈가 되고 있다. 현재 상용화되고 있는 대부분의 실시간 임베디드 시스템들을 다양한 측면에서 자사의 제품 기준에 따르는 기술적 특징과 성능 지표를 제공하고 있으며 이를 통한 성능 품질 등의 판단 근거로 제시되고 있다. 그러나 각 자료들이 공인 기관에서 제공되지 않아 자료의 신뢰도가 낮으며 따라서 성능측정 및 품질척도로 비교하는데는 어려움이 있다 따라서 본 연구는 이러한 문제점을 착안하여 자바 카드 API의 품질 평가를 위해서 사전에 테스트를 거쳐 좀더 신뢰성과 안전성이 높은 자바 카드 애플릿을 개발하고자 한다. 이에 가장 중요한 부분 중에 하나인 에러체크를 통해 임의적으로 에러 처리 시나리오를 작성해서 그 시나리오를 거친 테스트 데이터를 생성한 후, 그 데이터를 가지고 API의 풀질 평가를 하고자 하는데 목적이 있다. 이에 본 논문에서는 테스트 데이터 생성 시나리오를 제안하였다. 단지 제안만 해 놓았기 때문에 향후 이 시나리오를 거친 테스트 데이터 생성을 연구하고자 한다. 또한, Verification을 위해 수학에 기초를 둔 정형 기법(Formal Method)을 이용하여 좀더 깊이 있고, 정확한 검증을 수행 하고자 한다.

품질 인증 테스트는 소프트웨어 품질을 결정하고 보증하기 위하여 인증 기관에서 제품 개발 후에 수행하는 테스트이다. 제품 개발 완료 후에 매뉴얼과 소프트웨어 제품을 가지고 수행되므로 효과적으로 테스트가 수행되기 위해서는 제품의 매뉴얼과 제품에 대한 정확한 분석을 해야 하고 이를 바탕으로 테스트 데이터를 생성해야 한다. 따라서 본 연구에서는 매뉴얼 기반의 테스트 데이터 선정 기법을 제안한다. 본 연구에서 제안한 매뉴얼 기반의 테스트 분석 모델과 테스트 데이터 선정기법을 사용하면 보다 정확하고 체계적인 데스트를 수행 할 수 있을 것이다.

소프트웨어 테스트는 시스템의 신뢰도를 판단하는 중요한 작업이지만, 많은 노력과 비용이 요구된다. 모델 기반 테스트는 시스템 요구사항을 정형적으로 표현한 모델로부터 테스트 설계를 자동화함으로써 이러한 비용을 줄이기 위한 방안으로 제안되었다. 모델의 각 경로마다 입력값을 생성하여 테스트를 수행하는데, 이 때, 적절한 입력 값을 찾기 위해 메타-휴리스틱 기법을 사용한다. 본 논문은 슬라이싱 기법과 우선순위 정책을 적용한 테스트 데이터 자동 생성 기법을 제안하며, 목적 경로와 관련이 없는 변수를 제외하여 불필요한 계산을 억제한다. 실험을 통해 기존의 기법보다 효과적으로 테스트 데이터를 생성함을 보인다.

Software testing is important to determine the reliability of the system, a task that requires a lot of effort and cost. Model-based testing has been proposed as a way to reduce these costs by automating test designs from models that regularly represent system requirements. For each path of model to generate an input value to perform a test, meta-heuristic technique is used to find the test data. In this paper, we propose an automatic test data generation method using a slicing method and a priority policy, and suppress unnecessary computation by excluding variables not related to target path. And then, experimental results show that the proposed method generates test data more effectively than conventional method.