This thesis shows how to incorporate Unified Modeling Language sequence diagrams into genetic algorithm-based automated test case generators to increase the code coverage of their resulting test cases. Automated generation of test data through evolutionary testing was proven feasible in prior research studies. In those previous investigations, the metrics used for determining the test generation method effectiveness were the percentages of testing statement and branch code coverage achieved. However, the code coverage realized in those preceding studies often converged at suboptimal percentages due to a lack of guidance in conditional statements. This study compares the coverage percentages of 16 different Java programs when test cases are automatically generated with and without incorporating associated UML sequence diagrams. It introduces a tool known as the Evolutionary Test Case Generator, or ETCG, an automatic test case generator based on genetic algorithms that provides the ability to incorporate sequence diagrams to direct the heuristic search process and facilitate evolutionary testing. When the generator uses sequence diagrams, the resulting test cases showed an average improvement of 21% in branch coverage and 8% in statement coverage over test cases produced without using sequence diagrams. / Master of Science

This dissertation presents novel approaches to test context aware applications that suffer from a cost prohibitive number of context and GUI events and event combinations. The contributions of this work to test context aware applications under test include: (1) a real-world context events dataset from 82 Android users over a 30-day period, (2) applications of Markov models, Closed Sequential Pattern Mining (CloSPAN), Deep Neural Networks- Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), and Conditional Random Fields (CRF) applied to predict context patterns, (3) data driven test case generation techniques that insert events at the beginning of each test case in a round-robin manner, iterate through multiple context events at the beginning of each test case in a round-robin manner, and interleave real-world context event sequences and GUI events, and (4) systematically interleaving context with a combinatorial-based approach. The results of our empirical studies indicate (1) CRF outperforms other models thereby predicting context events with F1 score of about 60% for our dataset, (2) the ISFreqOne that iterates over context events at the beginning of each test case in a round-robin manner as well as interleaves real-world context event sequences and GUI events at an interval one achieves up to four times better code coverage than not including context, 0.06 times better coverage than RSContext that inserts random context events at the beginning of each test case, 0.05 times better coverage than ISContext that iterates over context events to insert at the beginning of each test case in a round-robin manner, and 0.04 times better coverage than ISFreqTwo that iterates over context events at the beginning of each test case in a round-robin manner as well as interleaves real-world context event sequences and GUI events at an interval two on an average across four subject applications and, (3) the PairwiseInterleaved technique that selects a different context event at the beginning of each test case by iterating through context covering array in a round-robin manner and systematically interleaves context with GUI events by prioritizing the execution of GUI events in new contexts achieves higher code coverage up to a factor of six when compared to Monkey, up to a factor of 1.3 when compared to a technique that generates test suites without context events, and similar code coverage when compared to ISContext that iterates over context events to insert at the beginning of each test case in a round-robin manner on an average across five subject applications.

This thesis investigates the application of Large Language Models (LLMs) in auto-mated test generation for software development, focusing on their challenges, bene-fits, and suitability for businesses. The study employs a mixed-methods approach, combining a literature review with empirical evaluations through surveys, interviews, and focus groups involving software developers and testers. Key findings indicate that LLMs enhance the efficiency and speed of test case generation, offering substantial improvements in test coverage and reducing development costs. However, the integration of LLMs poses several challenges, including technical complexities, the need for extensive customization, and concerns about the quality and reliability of the generated test cases. Additionally, ethical issues such as data biases and the potential impact on job roles were highlighted. The results show that while LLMs excel in generating test cases for routine tasks, their effectiveness diminishes in complex scenarios requiring deep domain knowledge and intricate system interactions. The study concludes that with proper training, continuous feedback, and iterative refinement, LLMs can be effectively integrated into existing workflows to complement traditional testing methods.

Symbolic execution-based test generation is gaining popularity for software test generation. The increasing complexity of the software program is posing new challenges in software execution-based test generation because of the path explosion problem. We present a new 2-phase symbolic execution driven strategy that achieves high branch coverage in software quickly. Phase 1 follows a greedy approach that quickly covers as many branches as possible by exploring each branch through its corresponding shortest path prefix. Phase 2 covers the remaining branches that are left uncovered if the shortest path to the branch was infeasible. In Phase 1, a basic conflict driven learning is used to skip all the paths that may have any of the earlier encountered conflicting conditions, while in Phase 2, a more intelligent conflict driven learning is used to skip regions that do not have a feasible path to any unexplored branch. This results in considerable reduction in unnecessary SMT solver calls. Experimental results show that significant speedup can be achieved, effectively reducing the time to detect a bug and providing higher branch coverage for a fixed time out period than previous techniques. / Master of Science

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Targeted software testing is a critical task in development of secure software. The core challenge of targeted software testing is to generate many inputs that reach specific code target locations in a given program. However, this task is challenging because it is NP-hard in theory and real-world programs contain very large input spaces and many lines of code, making this difficult in practice. In this thesis, I introduce a new approach for targeted software testing based on adaptive sampling. The key insight is to reduce the original problem to a sequence of approximate counting problems, and I apply this approach to targeted software testing in two stages. First, to find a single target-reaching input when no such input is given, I develop a new search algorithm MC2 that adaptively uses approximate-count feedback to narrow down which input region is more likely to contain a target-reaching input using probabilistic bisection. Second, given a single target-reaching input, I develop a new set approximation algorithm ProgramSampler that adaptively learns an approximation of the set of target-reaching inputs based on approximate-count feedback, where the set approximation can be efficiently uniformly sampled for many target-reaching inputs. Backed by theoretical guarantees, these techniques have been highly effective in practice: outperforming existing methods on average by 1-2 orders of magnitude.

A atividade de teste de software tem recebido considerável atenção de pesquisadores e engenheiros de software que reconhecem a sua utilidade na criação de produtos de qualidade. No entanto, os testes são caros e propensos a erros, o que impõe a necessidade de sistematizar e, portanto, a definição de técnicas para aumentar a qualidade e produtividade na sua condução. Várias técnicas de teste têm sido desenvolvidas e têm sido utilizadas, cada um com características próprias em termos de eficácia, custo, fases de aplicação, etc. Sistemas de realidade virtual frequentemente utilizam uma estrutura hierárquica denominada grafo de cena para representar as características dos objetos em um ambiente virtual tridimensional. Os grafos de cena também armazenam informações sobre o relacionamento entre os objetos, permitindo respostas adequadas ao usuário quando ocorrem interações. Neste trabalho, critérios de teste baseados em grafo de cena são estudados e definidos afim de aumentar a qualidade de aplicações de realidade virtual. Além disso, estudos de caso são apresentados, utilizando os critérios definidos aplicados a um framework de realidade virtual construído para gerar aplicações na área médica, além de utilizar uma aplicação de demonstração. Como forma de apoio aos critérios definidos foi desenvolvida uma ferramenta de teste capaz de verificar se os nós, que representam os objetos virtuais na cena, satisfazem seus requisitos conforme foram especificados / The activity of software testing has received considerable attention from researchers and software engineers who recognize its usefulness in creating quality products. However, the tests are expensive and prone to errors, which imposes the need to systematize and hence the definition of techniques to increase quality and productivity in their driving. Several testing techniques have been developed and have been used, each with its own characteristics in terms of effectiveness, cost, implementation stages, etc. Moreover, these techniques can also be adapted. In this work, test criteria based on scene graph are studied and defined in order to increase the quality of the Virtual Reality software. In addition, case studies are presented, using the criteria applied to a framework built to generate virtual reality applications in medicine, in addition to using a demo application. As a form of support to the criteria a testing tool was developed. It verifies whether the nodes that represent the virtual objects meet their requirements as they were specified