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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Heuristic generation of software test data

Holmes, Stephen Terry January 1996 (has links)
Incorrect system operation can, at worst, be life threatening or financially devastating. Software testing is a destructive process that aims to reveal software faults. Selection of good test data can be extremely difficult. To ease and assist test data selection, several test data generators have emerged that use a diverse range of approaches. Adaptive test data generators use existing test data to produce further effective test data. It has been observed that there is little empirical data on the adaptive approach. This thesis presents the Heuristically Aided Testing System (HATS), which is an adaptive test data generator that uses several heuristics. A heuristic embodies a test data generation technique. Four heuristics have been developed. The first heuristic, Direct Assignment, generates test data for conditions involving an input variable and a constant. The Alternating Variable heuristic determines a promising direction to modify input variables, then takes ever increasing steps in this direction. The Linear Predictor heuristic performs linear extrapolations on input variables. The final heuristic, Boundary Follower, uses input domain boundaries as a guide to locate hard-to-find solutions. Several Ada procedures have been tested with HATS; a quadratic equation solver, a triangle classifier, a remainder calculator and a linear search. Collectively they present some common and rare test data generation problems. The weakest testing criterion HATS has attempted to satisfy is all branches. Stronger, mutation-based criteria have been used on two of the procedures. HATS has achieved complete branch coverage on each procedure, except where there is a higher level of control flow complexity combined with non-linear input variables. Both branch and mutation testing criteria have enabled a better understanding of the test data generation problems and contributed to the evolution of heuristics and the development of new heuristics. This thesis contributes the following to knowledge: Empirical data on the adaptive heuristic approach to test data generation. How input domain boundaries can be used as guidance for a heuristic. An effective heuristic termination technique based on the heuristic's progress. A comparison of HATS with random testing. Properties of the test software that indicate when HATS will take less effort than random testing are identified.
2

Empirical study on strategy for Regression Testing

Hsu, Pai-Hung 03 August 2006 (has links)
Software testing plays a necessary role in software development and maintenance. This activity is performed to support quality assurance. It is very common to design a number of testing suite to test their programs manually for most test engineers. To design test data manually is an expensive and labor-wasting process. Base on this reason, how to generate software test data automatically becomes a hot issue. Most researches usually use the meta-heuristic search methods like genetic algorithm or simulated annealing to gain the test data. In most circumstances, test engineers will generate the test suite first if they have a new program. When they debug or change some code to become a new one, they still design another new test suite to test it. Nearly no people will reserve the first test data and reuse it. In this research, we want to discuss whether it is useful to store the original test data.
3

Evaluation of Test Data Generation Techniques for String Inputs

Li, Junyang, Xing, Xueer January 2017 (has links)
Context. The effective generation of test data is regarded as very important in the software testing. However, mature and effective techniques for generating string test data have seldom been explored due to the complexity and flexibility in the expression form of the string comparing to other data types. Objectives. Based on this problem, this study is to investigate strengths and limitations of existing string test data generation techniques to support future work for exploring an effective technique to generate string test data. This main goal was achieved via two objectives. First is investigating existing techniques for string test data generation; as well as finding out criteria and Classes-Under-Test (CUTs) used for evaluating the ability of string test generation. Second is to assess representative techniques through comparing effectiveness and efficiency. Methods. For the first objective, we used a systematic mapping study to collect data about existing techniques, criteria, and CUTs. With respect to the second objective, a comparison study was conducted to compare representative techniques selected from the results of systematic mapping study. The data from comparison study was analysed in a quantitative way by using statistical methods. Results. The existing techniques, criteria and CUTs which are related to string test generation were identified. A multidimensional categorisation was proposed to classify existing string test data generation techniques. We selected representative techniques from the search-based method, symbolic execution method, and random generation method of categorisation. Meanwhile, corresponding automated test generation tools including EvoSuite, Symbolic PathFinder (SPF), and Randoop, which achieved representative techniques, were selected to assess through comparing effectiveness and efficiency when applied to 21 CUTs. Conclusions. We concluded that: search-based method has the highest effectiveness and efficiency in three selected solution methods; random generation method has a low efficiency, but has a high fault-detecting ability for some specific CUTs; symbolic execution solution achieved by SPF cannot support string test generation well currently due to possibly incomplete string constraint solver or string generator.
4

Framework de geração de dados de teste para programas orientados a objetos / Test data generation framework for object-oriented software

Fernando Henrique Inocêncio Borba Ferreira 13 December 2012 (has links)
A geração de dados de teste é uma tarefa obrigatória do processo de teste de software. Em geral, é realizada por prossionais de teste, o que torna seu custo elevado e sua automatização necessária. Os frameworks existentes que auxiliam essa atividade são restritos, fornecendo apenas uma única técnica de geração de dados de teste, uma única função de aptidão para avaliação dos indivíduos e apenas um algoritmo de seleção. Este trabalho apresenta o framework JaBTeG (Java Bytecode Test Generation) de geração de dados de teste. A principal característica do framework é permitir o desenvolvimento de métodos de geração de dados de teste por meio da seleção da técnica de geração de dados de teste, da função de aptidão, do algoritmo de seleção e critério de teste estrutural. Utilizando o framework JaBTeG, técnicas de geração de dados de teste podem ser criadas e experimentadas. O framework está associado à ferramenta de teste JaBUTi (Java Bytecode Understanding and Testing) para auxiliar a geração de dados de teste. Quatro técnicas de geração de dados de teste, duas funções de aptidão e quatro algoritmos de seleção foram desenvolvidos para validação da abordagem proposta pelo framework. De maneira complementar, cinco programas com características diferentes foram testados com dados gerados usando os métodos providos pelo framework JaBTeG. / Test data generation is a mandatory activity of the software testing process. In general, it is carried out by testing practitioners, which makes it costly and its automation needed. Existing frameworks to support this activity are restricted, providing only one data generation technique, a single tness function to evaluate individuals, and a unique selection algorithm. This work describes the JaBTeG (Test Java Bytecode Generation) framework for testing data generation. The main characteristc of JaBTeG is to allow the development of data generation methods by selecting the data generation technique, the tness function, the selection algorithm and the structural testing criteria. By using JaBTeG, new methods for testing data generation can be developed and experimented. The framework was associated with JaBUTi (Java Bytecode Understanding and Testing) to support testing data creation. Four data generation techniques, two tness functions, and four selection algorithms were developed to validate the approach proposed by the framework. In addition, ve programs with dierent characteristics were tested with data generated using the methods supported by JaBTeG.
5

Framework de geração de dados de teste para programas orientados a objetos / Test data generation framework for object-oriented software

Ferreira, Fernando Henrique Inocêncio Borba 13 December 2012 (has links)
A geração de dados de teste é uma tarefa obrigatória do processo de teste de software. Em geral, é realizada por prossionais de teste, o que torna seu custo elevado e sua automatização necessária. Os frameworks existentes que auxiliam essa atividade são restritos, fornecendo apenas uma única técnica de geração de dados de teste, uma única função de aptidão para avaliação dos indivíduos e apenas um algoritmo de seleção. Este trabalho apresenta o framework JaBTeG (Java Bytecode Test Generation) de geração de dados de teste. A principal característica do framework é permitir o desenvolvimento de métodos de geração de dados de teste por meio da seleção da técnica de geração de dados de teste, da função de aptidão, do algoritmo de seleção e critério de teste estrutural. Utilizando o framework JaBTeG, técnicas de geração de dados de teste podem ser criadas e experimentadas. O framework está associado à ferramenta de teste JaBUTi (Java Bytecode Understanding and Testing) para auxiliar a geração de dados de teste. Quatro técnicas de geração de dados de teste, duas funções de aptidão e quatro algoritmos de seleção foram desenvolvidos para validação da abordagem proposta pelo framework. De maneira complementar, cinco programas com características diferentes foram testados com dados gerados usando os métodos providos pelo framework JaBTeG. / Test data generation is a mandatory activity of the software testing process. In general, it is carried out by testing practitioners, which makes it costly and its automation needed. Existing frameworks to support this activity are restricted, providing only one data generation technique, a single tness function to evaluate individuals, and a unique selection algorithm. This work describes the JaBTeG (Test Java Bytecode Generation) framework for testing data generation. The main characteristc of JaBTeG is to allow the development of data generation methods by selecting the data generation technique, the tness function, the selection algorithm and the structural testing criteria. By using JaBTeG, new methods for testing data generation can be developed and experimented. The framework was associated with JaBUTi (Java Bytecode Understanding and Testing) to support testing data creation. Four data generation techniques, two tness functions, and four selection algorithms were developed to validate the approach proposed by the framework. In addition, ve programs with dierent characteristics were tested with data generated using the methods supported by JaBTeG.
6

Techniques for Automatic Generation of Tests from Programs and Specifications

Edvardsson, Jon January 2006 (has links)
Software testing is complex and time consuming. One way to reduce the effort associated with testing is to generate test data automatically. This thesis is divided into three parts. In the first part a mixed-integer constraint solver developed by Gupta et. al is studied. The solver, referred to as the Unified Numerical Approach (una), is an important part of their generator and it is responsible for solving equation systems that correspond to the program path currently under test. In this thesis it is shown that, in contrast to traditional optimization methods, the una is not bounded by the size of the solved equation system. Instead, it depends on how the system is composed. That is, even for very simple systems consisting of one variable we can easily get more than a thousand iterations. It is also shown that the una is not complete, that is, it does not always find a mixed-integer solution when there is one. It is found that a better approach is to use a traditional optimization method, like the simplex method in combination with branch-and-bound and/or a cutting-plane algorithm as a constraint solver. The second part explores a specification-based approach for generating tests developed by Meudec. Tests are generated by partitioning the specification input domain into a set of subdomains using a rule-based automatic partitioning strategy. An important step of Meudec’s method is to reduce the number of generated subdomains and find a minimal partition. This thesis shows that Meudec’s minimal partition algorithm is incorrect. Furthermore, two new efficient alternative algorithms are developed. In addition, an algorithm for finding the upper and lower bound on the number of subdomains in a partition is also presented. Finally, in the third part, two different designs of automatic testing tools are studied. The first tool uses a specification as an oracle. The second tool, on the other hand, uses a reference program. The fault-detection effectiveness of the tools is evaluated using both randomly and systematically generated inputs.
7

Search-based software testing and complex test data generation in a dynamic programming language

Mairhofer, Stefan January 2008 (has links)
Manually creating test cases is time consuming and error prone. Search-based software testing (SBST) can help automate this process and thus to reduce time and effort and increase quality by automatically generating relevant test cases. Previous research have mainly focused on static programming languages with simple test data inputs such as numbers. In this work we present an approach for search-based software testing for dynamic programming languages that can generate test scenarios and both simple and more complex test data. This approach is implemented as a tool in and for the dynamic programming language Ruby. It uses an evolutionary algorithm to search for tests that gives structural code coverage. We have evaluated the system in an experiment on a number of code examples that differ in complexity and the type of input data they require. We compare our system with the results obtained by a random test case generator. The experiment shows, that the presented approach can compete with random testing and, for many situations, quicker finds tests and data that gives a higher structural code coverage.
8

A Systematic Review of Automated Test Data Generation Techniques / A Systematic Review of Automated Test Data Generation Techniques

Mahmood, Shahid January 2007 (has links)
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
9

Test data generation based on binary search for class-level testing

Beydeda, Sami, Gruhn, Volker 08 November 2018 (has links)
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.
10

Nástroj pro tvorbu obsahu databáze pro účely testování software / Test Data Generator for Relational Databases

Kotyz, Jan January 2018 (has links)
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.

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