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

No description available.

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.

Yang, Xiaoqing. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 113-120). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

No description available.

Ce travail de thèse, effectué dans la cadre du projet FUI Minalogic Bluesky, porte sur le test fonctionnel automatisé d'une classe particulière de contrôleurs logiques programmables (em4) produite par InnoVista Sensors. Ce sont des systèmes synchrones qui sont programmés au moyen d'un environnement de développement intégré (IDE). Les personnes qui utilisent et programment ces contrôleurs ne sont pas nécessairement des programmeurs experts. Le développement des applications logicielles doit être par conséquent simple et intuitif. Cela devrait également être le cas pour les tests. Même si les applications définies par ces utilisateurs ne sont pas nécessairement très critiques, il est important de les tester d'une manière adéquate et efficace. Un simulateur inclu dans l'IDE permet aux programmeurs de tester leurs programmes d'une façon qui reste à ce jour informelle et interactive en entrant manuellement des données de test. En se basant sur des recherches précédentes dans le domaine du test des programmes synchrones, nous proposons un nouveau langage de spécification de test, appelé SPTL (Synchronous Programs Testing Language) qui rend possible d'exprimer simplement des scénarios de test qui peuvent être exécutées à la volée pour générer automatiquement des séquences d'entrée de test. Il permet aussi de décrire l'environnement où évolue le système pour mettre des conditions sur les entrées afin d'arriver à des données de test réalistes et de limiter celles qui sont inutiles. SPTL facilite cette tâche de test en introduisant des notions comme les profils d'utilisation, les groupes et les catégories. Nous avons conçu et développé un prototype, nommé "Testium", qui traduit un programme SPTL en un ensemble de contraintes exploitées par un solveur Prolog qui choisit aléatoirement les entrées de test. La génération de données de test s'appuie ainsi sur des techniques de programmation logique par contraintes. Pour l'évaluer, nous avons expérimenté cette méthode sur des exemples d'applications EM4 typiques et réels. Bien que SPTL ait été évalué sur em4, son utilisation peut être envisagée pour la validation d'autres types de contrôleurs ou systèmes synchrones. / This thesis work done in the context of the FUI project Minalogic Bluesky, concerns the automated functional testing of a particular class of programmable logic controllers (em4) produced by InnoVista Sensors. These are synchronous systems that are programmed by means of an integrated development environment (IDE). People who use and program these controllers are not necessarily expert programmers. The development of software applications should be as result simple and intuitive. This should also be the case for testing. Although applications defined by these users need not be very critical, it is important to test them adequately and effectively. A simulator included in the IDE allows programmers to test their programs in a way that remains informal and interactive by manually entering test data.Based on previous research in the area of synchronous test programs, we propose a new test specification language, called SPTL (Synchronous Testing Programs Language) which makes possible to simply express test scenarios that can be executed on the fly to automatically generate test input sequences. It also allows describing the environment in which the system evolves to put conditions on inputs to arrive to realistic test data and limit unnecessary ones. SPTL facilitates this testing task by introducing concepts such as user profiles, groups and categories. We have designed and developed a prototype named "Testium", which translates a SPTL program to a set of constraints used by a Prolog solver that randomly selects the test inputs. So, generating test data is based on constraint logic programming techniques.To assess this, we experimented this method on realistic and typical examples of em4 applications. Although SPTL was evaluated on EM4, its use can be envisaged for the validation of other types of synchronous controllers or systems.

The subject of this diploma thesis is the design of a single-purpose machine for automatic testing of pneumatic fittings. Pneumatic fittings used in brake systems are subject to high technical requirements and 100% tightness control in production. In this case, manual testing is not effective and the goal is to automate the process. The theoretical part presents pneumatic mechanisms, analysis of the tested pneumatic fitting with technical parameters, the possibility of tightness testing, use of sensors, and rotary tables. In the practical part, a systematic analysis of the problem was performed, according to which the overall design proceeded. Subsequently, the design of the complete machine containing individual nodes is processed, supplemented by the necessary calculations. The conclusion of the thesis contains an evaluation of the whole project.

Manufacture testing of digital integrated circuits is essential for high quality. However, exhaustive testing is impractical, and only a small subset of all possible test patterns (or test pattern pairs) may be applied. Thus, it is crucial to choose a subset that detects a high percentage of the defective parts and produces a low defective part level. Historically, test pattern generation has often been seen as a deterministic endeavor. Test sets are generated to deterministically ensure that a large percentage of the targeted faults are detected. However, many real defects do not behave like these faults, and a test set that detects them all may still miss many defects. Unfortunately, modeling all possible defects as faults is impractical. Thus, it is important to fortuitously detect unmodeled defects using high quality test sets. To maximize fortuitous detection, we do not assume a high correlation between faults and actual defects. Instead, we look at the common requirements for all defect detection. We deterministically maximize the observations of the leastobserved sites while randomly exciting the defects that may be present. The resulting decrease in defective part level is estimated using the MPGD model. This dissertation describes the MPGD defective part level model and shows how it can be used to predict defective part levels resulting from static defect detection. Unlike many other predictors, its predictions are a function of site observations, not fault coverage, and thus it is generally more accurate at high fault coverages. Furthermore, its components model the physical realities of site observation and defect excitation, and thus it can be used to give insight into better test generation strategies. Next, we investigate the effect of additional constraints on the fortuitous detection of defects-specifically, as we focus on detecting dynamic defects instead of static ones. We show that the quality of the randomness of excitation becomes increasingly important as defect complexity increases. We introduce a new metric, called excitation balance, to estimate the quality of the excitation, and we show how excitation balance relates to the constant τ in the MPGD model.

A paradigm shift is emerging in system reliability and maintainability. The military and industrial sectors are moving away from the traditional breakdown and scheduled maintenance to adopt concepts referred to as Condition Based Maintenance (CBM) and Prognostic Health Management (PHM). In addition to signal processing and subsequent diagnostic and prognostic algorithms these new technologies involve storage of large volumes of both quantitative and qualitative information to carry out maintenance tasks effectively. This not only requires research and development in advanced technologies but also the means to store, organize and access this knowledge in a timely and efficient fashion. Knowledge-based expert systems have been shown to possess capabilities to manage vast amounts of knowledge, but an intelligent systems approach calls for attributes like learning and adaptation in building autonomous decision support systems. This research presents an integrated knowledge-based approach to diagnostic reasoning for CBM of engineering systems. A two level diagnosis scheme has been conceptualized in which first a fault is hypothesized using the observational symptoms from the system and then a more specific diagnostic test is carried out using only the relevant sensor measurements to confirm the hypothesis. Utilizing the qualitative (textual) information obtained from these systems in combination with quantitative (sensory) information reduces the computational burden by carrying out a more informed testing. An Industrial Language Processing (ILP) technique has been developed for processing textual information from industrial systems. Compared to other automated methods that are computationally expensive, this technique manipulates standardized language messages by taking advantage of their semi-structured nature and domain limited vocabulary in a tractable manner. A Dynamic Case-based reasoning (DCBR) framework provides a hybrid platform for diagnostic reasoning and an integration mechanism for the operational infrastructure of an autonomous Decision Support System (DSS) for CBM. This integration involves data gathering, information extraction procedures, and real-time reasoning frameworks to facilitate the strategies and maintenance of critical systems. As a step further towards autonomy, DCBR builds on a self-evolving knowledgebase that learns from its performance feedback and reorganizes itself to deal with non-stationary environments. A unique Human-in-the-Loop Learning (HITLL) approach has been adopted to incorporate human feedback in the traditional Reinforcement Learning (RL) algorithm.

Manufacture testing of digital integrated circuits is essential for high quality. However, exhaustive testing is impractical, and only a small subset of all possible test patterns (or test pattern pairs) may be applied. Thus, it is crucial to choose a subset that detects a high percentage of the defective parts and produces a low defective part level. Historically, test pattern generation has often been seen as a deterministic endeavor. Test sets are generated to deterministically ensure that a large percentage of the targeted faults are detected. However, many real defects do not behave like these faults, and a test set that detects them all may still miss many defects. Unfortunately, modeling all possible defects as faults is impractical. Thus, it is important to fortuitously detect unmodeled defects using high quality test sets. To maximize fortuitous detection, we do not assume a high correlation between faults and actual defects. Instead, we look at the common requirements for all defect detection. We deterministically maximize the observations of the leastobserved sites while randomly exciting the defects that may be present. The resulting decrease in defective part level is estimated using the MPGD model. This dissertation describes the MPGD defective part level model and shows how it can be used to predict defective part levels resulting from static defect detection. Unlike many other predictors, its predictions are a function of site observations, not fault coverage, and thus it is generally more accurate at high fault coverages. Furthermore, its components model the physical realities of site observation and defect excitation, and thus it can be used to give insight into better test generation strategies. Next, we investigate the effect of additional constraints on the fortuitous detection of defects-specifically, as we focus on detecting dynamic defects instead of static ones. We show that the quality of the randomness of excitation becomes increasingly important as defect complexity increases. We introduce a new metric, called excitation balance, to estimate the quality of the excitation, and we show how excitation balance relates to the constant τ in the MPGD model.