Automated Test Data Generation (ATDG) is an activity that in the course of software testing automatically generates test data for the software under test (SUT). It usually makes the testing more efficient and cost effective. Test Data Generation (TDG) is crucial for software testing because test data is one of the key factors for determining the quality of any software test during its execution. The multi-phased activity of ATDG involves various techniques for each of its phases. This research field is not new by any means, albeit lately new techniques have been devised and a gradual increase in the level of maturity has brought some diversified trends into it. To this end several ATDG techniques are available, but emerging trends in computing have raised the necessity to summarize and assess the current status of this area particularly for practitioners, future researchers and students. Further, analysis of the ATDG techniques becomes even more important when Miller et al. [4] highlight the hardship in general acceptance of these techniques. Under this scenario only a systematic review can address the issues because systematic reviews provide evaluation and interpretation of all available research relevant to a particular research question, topic area, or phenomenon of interest. This thesis, by using a trustworthy, rigorous, and auditable methodology, provides a systematic review that is aimed at presenting a fair evaluation of research concerning ATDG techniques of the period 1997-2006. Moreover it also aims at identifying probable gaps in research about ATDG techniques of defined period so as to suggest the scope for further research. This systematic review is basically presented on the pattern of [5 and 8] and follows the techniques suggested by [1].The articles published in journals and conference proceedings during the defined period are of concern in this review. The motive behind this selection is quite logical in the sense that the techniques that are discussed in literature of this period might reflect their suitability for the prevailing software environment of today and are believed to fulfill the needs of foreseeable future. Furthermore only automated and/or semiautomated ATDG techniques have been chosen for consideration while leaving the manual techniques as they are out of the scope. As a result of the preliminary study the review identifies ATDG techniques and relevant articles of the defined period whereas the detailed study evaluates and interprets all available research relevant to ATDG techniques. For interpretation and elaboration of the discovered ATDG techniques a novel approach called ‘Natural Clustering’ is introduced. To accomplish the task of systematic review a comprehensive research method has been developed. Then on the practical implications of this research method important results have been gained. These results have been presented in statistical/numeric, diagrammatic, and descriptive forms. Additionally the thesis also introduces various criterions for classification of the discovered ATDG techniques and presents a comprehensive analysis of the results of these techniques. Some interesting facts have also been highlighted during the course of discussion. Finally, the discussion culminates with inferences and recommendations which emanate from this analysis. As the research work produced in the thesis is based on a rich amount of trustworthy information, therefore, it could also serve the purpose of being an upto- date guide about ATDG techniques. / Shahid Mahmood Folkparksvägen 14:23 372 40 Ronneby Sweden +46 76 2971676

This thesis deals with the problematic of test-data generation for relational databases. The aim of the this thesis is to design and implement tool which meets defined constrains and allows us to generate test-data. This tool uses SMT solver for constraint solving and test-data generation.

One of the important tasks during software testing is the generation of appropriate test data. Various techniques have been proposed to automate this task. The techniques available, however, often have problems limiting their use. In the case of dynamic test data generation techniques, a frequent problem is that a large number of iterations might be necessary to obtain test data. This article proposes a novel technique for automated test data generation based on binary search. Binary search conducts searching tasks in logarithmic time, as long as its assumptions are fulfilled. This article shows that these assumptions can also be fulfilled in the case of path-oriented test data generation and presents a technique which can be used to generate test data covering certain paths in class methods.

Software testing is a part of software development process. However, this part is the first one to miss by software developers if there is a limited time to complete the project. Software developers often finish their software construction closed to the delivery time, they usually don¿t have enough time to create effective test cases for testing their programs. Creating test cases manually is a huge work for software developers in the rush hours. A tool which automatically generates test cases and test data can help the software developers to create test cases from software designs/models in early stage of the software development (before coding). Heuristic techniques can be applied for creating quality test data. In this paper, a GA-based test data generation technique has been proposed to generate test data from UML state diagram, so that test data can be generated before coding. The paper details the GA implementation to generate sequences of triggers for UML state diagram as test cases. The proposed algorithm has been demonstrated manually for an example of a vending machine.

Automatic test data generation helps testers to validate software against user requirements more easily. Test data can be generated from many sources; for example, experience of testers, source program, or software specification. Selecting a proper test data set is a decision making task. Testers have to decide what test data that they should use, and a heuristic technique is needed to solve this problem automatically. In this paper, we propose a framework for generating test data from software specifications. The selected specification is Unified Modeling Language (UML) state machine diagram. UML state machine diagram describes a system in term of state which can be changed when there is an action occurring in the system. The generated test data is a sequence of these actions. These sequences of action help testers to know how they should test the system. The quality of generated test data is measured by the number of transitions which is fired using the test data. The more transitions test data can fire, the better quality of test data is. The number of coverage transitions is also used as a feedback for a heuristic search for a better test set. Genetic algorithms (GAs) are selected for searching the best test data. Our experimental results show that the proposed GA-based approach can work well for generating test data for some types of UML state machine diagrams.

The extended finite state machine (EFSM) is a powerful modelling approach that has been applied to represent a wide range of systems. Despite its popularity, testing from an EFSM is a substantial problem for two main reasons: path feasibility and path test case generation. The path feasibility problem concerns generating transition paths through an EFSM that are feasible and satisfy a given test criterion. In an EFSM, guards and assignments in a path‟s transitions may cause some selected paths to be infeasible. The problem of path test case generation is to find a sequence of inputs that can exercise the transitions in a given feasible path. However, the transitions‟ guards and assignments in a given path can impose difficulties when producing such data making the range of acceptable inputs narrowed down to a possibly tiny range. While search-based approaches have proven efficient in automating aspects of testing, these have received little attention when testing from EFSMs. This thesis proposes an integrated search-based approach to automatically test from an EFSM. The proposed approach generates paths through an EFSM that are potentially feasible and satisfy a test criterion. Then, it generates test cases that can exercise the generated feasible paths. The approach is evaluated by being used to test from five EFSM cases studies. The achieved experimental results demonstrate the value of the proposed approach.

Software testing is a tedious and very expensive undertaking. Automatic test data generation is, therefore, proposed in this research to help testers reduce their work as well as ascertain software quality. The concept of test driven development (TDD) has become increasingly popular during the past several years. According to TDD, test data should be prepared before the beginning of code implementation. Therefore, this research asserts that the test data should be generated from the software design documents which are normally created prior to software code implementation. Among such design documents, the UML state machine diagrams are selected as a platform for the proposed automated test data generation mechanism. Such diagrams are selected because they show behaviours of a single object in the system. The genetic algorithm (GA) based approach has been developed and applied in the process of searching for the right amount of quality test data. Finally, the generated test data have been used together with UML class diagrams for JUnit test code generation. The GA-based test data generation methods have been enhanced to take care of parallel path and loop problems of the UML state machines. In addition the proposed GA-based approach is also targeted to solve the diagrams with parameterised triggers. As a result, the proposed framework generates test data from the basic state machine diagram and the basic class diagram without any additional nonstandard information, while most other approaches require additional information or the generation of test data from other formal languages. The transition coverage values for the introduced approach here are also high; therefore, the generated test data can cover most of the behaviour of the system. / EU Asia-Link project TH/Asia Link/004(91712) East-West and CAMT

Software testing is a tedious and very expensive undertaking. Automatic test data generation is, therefore, proposed in this research to help testers reduce their work as well as ascertain software quality. The concept of test driven development (TDD) has become increasingly popular during the past several years. According to TDD, test data should be prepared before the beginning of code implementation. Therefore, this research asserts that the test data should be generated from the software design documents which are normally created prior to software code implementation. Among such design documents, the UML state machine diagrams are selected as a platform for the proposed automated test data generation mechanism. Such diagrams are selected because they show behaviours of a single object in the system. The genetic algorithm (GA) based approach has been developed and applied in the process of searching for the right amount of quality test data. Finally, the generated test data have been used together with UML class diagrams for JUnit test code generation. The GA-based test data generation methods have been enhanced to take care of parallel path and loop problems of the UML state machines. In addition the proposed GA-based approach is also targeted to solve the diagrams with parameterised triggers. As a result, the proposed framework generates test data from the basic state machine diagram and the basic class diagram without any additional nonstandard information, while most other approaches require additional information or the generation of test data from other formal languages. The transition coverage values for the introduced approach here are also high; therefore, the generated test data can cover most of the behaviour of the system.

O teste de mutação é um critério de teste poderoso para detectar falhas e medir a eficácia de um conjunto de dados de teste. No entanto, é uma técnica de teste computacionalmente cara. O alto custo provém principalmente do esforço para gerar dados de teste adequados para matar os mutantes e pela existência de mutantes equivalentes. Nesse contexto, o objetivo desta tese é apresentar uma abordagem chamada de Reach, Infect and Propagation to Mutation Testing (RIPMuT) que visa gerar dados de teste e sugerir mutantes equivalentes. A abordagem é composta por dois módulos: (i) uma geração automatizada de dados de teste usando subida da encosta e um esquema de fitness de acordo com as condições de alcançabilidade, infeção e propagação (RIP); e (ii) um método para sugerir mutantes equivalentes com base na análise das condições RIP durante o processo de geração de dados de teste. Os experimentos foram conduzidos para avaliar a eficácia da abordagem RIP-MuT e um estudo comparativo com o algoritmo genético e testes aleatórios foi realizado. A abordagem RIP-MuT obteve um escore médio de mutação de 18,25 % maior que o AG e 35,93 % maior que o teste aleatório. O método proposto para detecção de mutantes equivalentes se mostrou viável para redução de custos relacionado a essa atividade, uma vez que obteve uma precisão de 75,05% na sugestão dos mutantes equivalentes. Portanto, os resultados indicam que a abordagem gera dados de teste adequados capazes de matar a maioria dos mutantes em programas C e, também auxilia a identificar mutantes equivalentes corretamente. / Mutation Testing is a powerful test criterion to detect faults and measure the effectiveness of a test data set. However, it is a computationally expensive testing technique. The high cost comes mainly from the effort to generate adequate test data to kill the mutants and by the existence of equivalent mutants. In this thesis, an approach called Reach, Infect and Propagation to Mutation Testing (RIP-MuT) is presented to generate test data and to suggest equivalent mutants. The approach is composed of two modules: (i) an automated test data generation using hill climbing and a fitness scheme according to Reach, Infect, and Propagate (RIP) conditions; and (ii) a method to suggest equivalent mutants based on the analyses of RIP conditions during the process of test data generation. The experiments were conducted to evaluate the effectiveness of the RIP-MuT approach and a comparative study with a genetic algorithm and random testing. The RIP-MuT approach achieved a mean mutation score of 18.25% higher than the GA and 35.93% higher than random testing. The proposed method for detection of equivalent mutants demonstrate to be feasible for cost reduction in this activity since it obtained a precision of 75.05% on suggesting equivalent mutants. Therefore, the results indicate that the approach produces effective test data able to strongly kill the majority of mutants on C programs, and also it can assist in suggesting equivalent mutants correctly.

Produtos de software com diferentes complexidades são criados diariamente através da elicitação de demandas complexas e variadas juntamente a prazos restritos. Enquanto estes surgem, altos níveis de qualidade são esperados para tais, ou seja, enquanto os produtos tornam-se mais complexos, o nível de qualidade pode não ser aceitável enquanto o tempo hábil para testes não acompanha a complexidade. Desta maneira, o teste de software e a geração automática de dados de testes surgem com o intuito de entregar produtos contendo altos níveis de qualidade mediante baixos custos e rápidas atividades de teste. Porém, neste contexto, os profissionais de desenvolvimento dependem das estratégias de geração automáticas de testes e principalmente da seleção da técnica mais adequada para conseguir maior cobertura de código possível, este é um fator importante dados que cada técnica de geração de dados de teste possui particularidades e problemas que fazem seu uso melhor em determinados tipos de software. A partir desde cenário, o presente trabalho propõe a seleção da técnica adequada para cada classe de um software com base em suas características, expressas por meio de métricas de softwares orientados a objetos a partir do algoritmo de classificação Naive Bayes. Foi realizada uma revisão bibliográfica de dois algoritmos de geração, algoritmo de busca aleatório e algoritmo de busca genético, compreendendo assim suas vantagens e desvantagens tanto de implementação como de execução. As métricas CK também foram estudadas com o intuito de compreender como estas podem descrever melhor as características de uma classe. O conhecimento adquirido possibilitou coletar os dados de geração de testes de cada classe como cobertura de código e tempo de geração a partir de cada técnica e também as métricas CK, permitindo assim a análise destes dados em conjunto e por fim execução do algoritmo de classificação. Os resultados desta análise demonstraram que um conjunto reduzido e selecionado das métricas CK é mais eficiente e descreve melhor as características de uma classe se comparado ao uso do conjunto por completo. Os resultados apontam também que as métricas CK não influenciam o tempo de geração dos dados de teste, entretanto, as métricas CK demonstraram correlação moderada e influência na seleção do algoritmo genético, participando assim na sua seleção pelo algoritmo Naive Bayes / Software products with different complexity are created daily through analysis of complex and varied demands together with tight deadlines. While these arise, high levels of quality are expected for such, as products become more complex, the quality level may not be acceptable while the timing for testing does not keep up with complexity. In this way, software testing and automatic generation of test data arise in order to deliver products containing high levels of quality through low cost and rapid test activities. However, in this context, software developers depend on the strategies of automatic generation of tests and especially on the selection of the most adequate technique to obtain greater code coverage possible, this is an important factor given that each technique of data generation of test have peculiarities and problems that make its use better in certain types of software. From this scenario, the present work proposes the selection of the appropriate technique for each class of software based on its characteristics, expressed through object oriented software metrics from the naive bayes classification algorithm. Initially, a literature review of the two generation algorithms was carried out, random search algorithm and genetic search algorithm, thus understanding its advantages and disadvantages in both implementation and execution. The CK metrics have also been studied in order to understand how they can better describe the characteristics of a class. The acquired knowledge allowed to collect the generation data of tests of each class as code coverage and generation time from each technique and also the CK metrics, thus allowing the analysis of these data together and finally execution of the classification algorithm. The results of this analysis demonstrated that a reduced and selected set of metrics is more efficient and better describes the characteristics of a class besides demonstrating that the CK metrics have little or no influence on the generation time of the test data and on the random search algorithm . However, the CK metrics showed a medium correlation and influence in the selection of the genetic algorithm, thus participating in its selection by the algorithm naive bayes