Das bereits existierende Konzept zur Bewertung der Anonymisierung von Testdaten wird in dieser Arbeit weiter untersucht. Dabei zeigen sich die Vor- und Nachteile gegenüber bereits existierenden Distanzmetriken. Weiterführend wird untersucht, welchen Einfluss Parameteränderungen auf die Ergebnisse haben.

När nya funktioner utvecklas måste funktionerna testas utifrån den givna kravspecifikationen för att garantera en korrekt implementering. Testning är en del av utvecklingsprocessen och en viktig komponent som behövs för att förbättra tillförlitligheten av system. Testdata är den data som används för att utföra specifika testfall. Företagets testdata är riktiga produktionsdata, nämligen personuppgifter, som avidentifieras med pseudonymisering för att minska länkbarheten till den fysiska individen. Pseudonymisering tillämpas dock inte på personnummer, då företaget behöver riktiga personnummer för att utföra sina tester. Det skapas en osäkerhet när riktiga personuppgifter används vid testsammanhang, eftersom GDPR skyddar enskilda individers grundläggande rättigheter och beskriver individers rätt till skydd av personuppgifter (Integritetsskyddsmyndigheten 2021b). Företaget har idag riktlinjer och organisatoriska skyddsåtgärder som följs av anställda för att i den utsträckning det går arbeta i enlighet med GDPR. Testdata som inkluderar personnummer är problematiskt att arbeta med och som nyanställd kan det bli utmanande att lära sig hur en korrekt hantering genomförs om det inte finns ett processdokument som beskriver hur testdata hanteras hos företaget.  En fallstudie har genomförts för att konstruera ett processdokument med hjälp av kunniga anställda på företaget. Syftet med fallstudien var att grundligt dokumenteratestdatahanteringen, i syfte att hitta förbättringsförslag för att i framtiden öka efterlevnaden av GDPR. För att ta reda på hur processdokument ökar efterlevnaden av GDPR har en kvalitativ ansats tillämpats för att utvärdera innehållet i det konstruerade processdokumentet. Åtta semistrukturerade intervjuer har genomförts med testare för att fånga deras tankar och åsikter kring processdokumentet. Resultatet indikerar att det konstruerade processdokumentet beskriver en hantering som företaget strävar efter att uppnå i framtiden. Det framgår även att de som har mest nytta av processdokumentet är nyanställda. / <p>Stavningsvarierad titel:</p><p>Process document on test data handling that enhances GDPR compliance</p>

The main part in the testing of the software is in the generation of test cases suitable for software system testing. The quality of the test cases plays a major role in reducing the time of software system testing and subsequently reduces the cost. The test cases, in model de- sign stages, are used to detect the faults before implementing it. This early detection offers more flexibility to correct the faults in early stages rather than latter ones. The best of these tests, that covers both static and dynamic software system model specifications, is one of the chal- lenges in the software testing. The static and dynamic specifications could be represented efficiently by Unified Modelling Language (UML) class diagram and sequence diagram. The work in this thesis shows that High Level Petri Nets (HLPN) can represent both of them in one model. Using a proper model in the representation of the software specifications is essential to generate proper test cases. The research presented in this thesis introduces novel and automated test cases generation techniques that can be used within a software sys- tem design testing. Furthermore, this research introduces e cient au- tomated technique to generate a formal software system model (HLPN) from semi-formal models (UML diagrams). The work in this thesis con- sists of four stages: (1) generating test cases from class diagram and Object Constraint Language (OCL) that can be used for testing the software system static specifications (the structure) (2) combining class diagram, sequence diagram and OCL to generate test cases able to cover both static and dynamic specifications (3) generating HLPN automat- ically from single or multi sequence diagrams (4) generating test cases from HLPN. The test cases that are generated in this work covered the structural and behavioural of the software system model. In first two phases of this work, the class diagram and sequence diagram are decomposed to nodes (edges) which are linked by Classes Hierarchy Table (CHu) and Edges Relationships Table (ERT) as well. The linking process based on the classes and edges relationships. The relationships of the software system components have been controlled by consistency checking technique, and the detection of these relationships has been automated. The test cases were generated based on these interrelationships. These test cases have been reduced to a minimum number and the best test case has been selected in every stage. The degree of similarity between test cases is used to ignore the similar test cases in order to avoid the redundancy. The transformation from UML sequence diagram (s) to HLPN facilitates the simpli cation of software system model and introduces formal model rather than semi-formal one. After decomposing the sequence diagram to Combined Fragments, the proposed technique converts each Combined Fragment to the corresponding block in HLPN. These blocks are con- nected together in Combined Fragments Net (CFN) to construct the the HLPN model. The experimentations with the proposed techniques show the effectiveness of these techniques in covering most of the software system specifications.

As atividades de teste de software são de crescente importância devido à maciça presença de sistemas de informação em nosso cotidiano. Programas de Processamento de Imagens (PI) têm um domínio de entrada bastante complexo e, por essa razão, o teste tradicional realizado com esse tipo de programa, conduzido majoritariamente de forma manual, é uma tarefa de alto custo e sujeita a imperfeições. No teste tradicional, em geral, as imagens de entrada são construídas manualmente pelo testador ou selecionadas aleatoriamente de bases de imagens, muitas vezes dificultando a revelação de defeitos no software. A partir de um mapeamento sistemático da literatura realizado, foi identificada uma lacuna no que se refere à geração automatizada de dados de teste no domínio de imagens. Assim, o objetivo desta pesquisa é propor uma abordagem - denominada TAIGA (Test imAge generatIon by Genetic Algorithm) - para a geração de dados de teste para programas de PI por meio de algoritmo genético. Na abordagem proposta, operadores genéticos tradicionais (mutação e crossover) são adaptados para o domínio de imagens e a função fitness é substituída por uma avaliação de resultados provenientes de teste de mutação. A abordagem TAIGA foi validada por meio de experimentos com oito programas de PI distintos, nos quais observaram-se ganhos de até 38,61% em termos de mutation score em comparação ao teste tradicional. Ao automatizar a geração de dados de teste, espera-se conferir maior qualidade ao desenvolvimento de sistemas de PI e contribuir com a diminuição de custos com as atividades de teste de software neste domínio / The massive presence of information systems in our lives has been increasing the importance of software test activities. Image Processing (IP) programs have very complex input domains and, therefore, the traditional testing for this kind of program is a highly costly and vulnerable to errors task. In traditional testing, usually, testers create images by themselves or they execute random selection from images databases, which can make it harder to reveal faults in the software under test. In this context, a systematic mapping study was conducted and a gap was identified concerning the automated test data generation in the images domain. Thus, an approach for generating test data for IP programs by means of genetic algorithms was proposed: TAIGA - Test imAge generatIon by Genetic Algorithm. This approach adapts traditional genetic operators (mutation and crossover) to the images domain and replaces the fitness function by the evaluation of the results of mutation testing. The proposed approach was validated by the execution of experiments involving eight distinct IP programs. TAIGA was able to provide up to 38.61% increase in mutation score when compared to the traditional testing for IP programs. It\'s expected that the automation of test data generation elevates the quality of image processing systems development and reduces the costs of software test activities in the images domain

Veículo autônomo inteligente (ou apenas veículo autônomo VA) é um tipo de sistema embarcado que integra componentes físicos (hardware) e computacionais (software). Sua principal característica é a capacidade de locomoção e de operação de modo semi ou completamente autônomo. A autonomia cresce com a capacidade de percepção e de deslocamento no ambiente, robustez e capacidade de resolver e executar tarefas lidando com as mais diversas situações (inteligência). Veículos autônomos representam um tópico de pesquisa importante e que tem impacto direto na sociedade. No entanto, à medida que esse campo avança alguns problemas secundários aparecem como, por exemplo, como saber se esses sistemas foram suficientemente testados. Uma das fases do teste de um VA é o teste de campo, em que o veículo é levado para um ambiente pouco controlado e deve executar livremente a missão para a qual foi programado. Ele é geralmente utilizado para garantir que os veículos autônomos mostrem o comportamento desejado, mas nenhuma informação sobre a estrutura do código é utilizada. Pode ocorrer que o veículo (hardware e software) passou no teste de campo, mas trechos importantes do código nunca tenham sido executados. Durante o teste de campo, os dados de entrada são coletados em logs que podem ser posteriormente analisados para avaliar os resultados do teste e para realizar outros tipos de teste offline. Esta tese apresenta um conjunto de propostas para apoiar a análise do teste de campo do ponto de vista do teste estrutural. A abordagem é composta por um modelo de classes no contexto do teste de campo, uma ferramenta que implementa esse modelo e um algoritmo genético para geração de dados de teste. Apresenta também heurísticas para reduzir o conjunto de dados contidos em um log sem diminuir substancialmente a cobertura obtida e estratégias de combinação e mutação que são usadas no algoritmo. Estudos de caso foram conduzidos para avaliar as heurísticas e estratégias e são também apresentados e discutidos. / Intelligent autonomous vehicle (or just autonomous vehicle - AV) is a type of embedded system that integrates physical (hardware) and computational (software) components. Its main feature is the ability to move and operate partially or fully autonomously. Autonomy grows with the ability to perceive and move within the environment, robustness and ability to solve and perform tasks dealing with different situations (intelligence). Autonomous vehicles represent an important research topic that has a direct impact on society. However, as this field progresses some secondary problems arise, such as how to know if these systems have been sufficiently tested. One of the testing phases of an AV is the field testing, where the vehicle is taken to a controlled environment and it should execute the mission for which it was programed freely. It is generally used to ensure that autonomous vehicles show the intended behavior, but it usually does not take into consideration the code structure. The vehicle (hardware and software) could pass the field testing, but important parts of the code may never have been executed. During the field testing, the input data are collected in logs that can be further analyzed to evaluate the test results and to perform other types of offline tests. This thesis presents a set of proposals to support the analysis of field testing from the point of view of the structural testing. The approach is composed of a class model in the context of the field testing, a tool that implements this model and a genetic algorithm to generate test data. It also shows heuristics to reduce the data set contained in a log without reducing substantially the coverage obtained and combination and mutation strategies that are used in the algorithm. Case studies have been conducted to evaluate the heuristics and strategies, and are also presented and discussed.

As atividades de teste de software são de crescente importância devido à maciça presença de sistemas de informação em nosso cotidiano. Programas de Processamento de Imagens (PI) têm um domínio de entrada bastante complexo e, por essa razão, o teste tradicional realizado com esse tipo de programa, conduzido majoritariamente de forma manual, é uma tarefa de alto custo e sujeita a imperfeições. No teste tradicional, em geral, as imagens de entrada são construídas manualmente pelo testador ou selecionadas aleatoriamente de bases de imagens, muitas vezes dificultando a revelação de defeitos no software. A partir de um mapeamento sistemático da literatura realizado, foi identificada uma lacuna no que se refere à geração automatizada de dados de teste no domínio de imagens. Assim, o objetivo desta pesquisa é propor uma abordagem - denominada TAIGA (Test imAge generatIon by Genetic Algorithm) - para a geração de dados de teste para programas de PI por meio de algoritmo genético. Na abordagem proposta, operadores genéticos tradicionais (mutação e crossover) são adaptados para o domínio de imagens e a função fitness é substituída por uma avaliação de resultados provenientes de teste de mutação. A abordagem TAIGA foi validada por meio de experimentos com oito programas de PI distintos, nos quais observaram-se ganhos de até 38,61% em termos de mutation score em comparação ao teste tradicional. Ao automatizar a geração de dados de teste, espera-se conferir maior qualidade ao desenvolvimento de sistemas de PI e contribuir com a diminuição de custos com as atividades de teste de software neste domínio / The massive presence of information systems in our lives has been increasing the importance of software test activities. Image Processing (IP) programs have very complex input domains and, therefore, the traditional testing for this kind of program is a highly costly and vulnerable to errors task. In traditional testing, usually, testers create images by themselves or they execute random selection from images databases, which can make it harder to reveal faults in the software under test. In this context, a systematic mapping study was conducted and a gap was identified concerning the automated test data generation in the images domain. Thus, an approach for generating test data for IP programs by means of genetic algorithms was proposed: TAIGA - Test imAge generatIon by Genetic Algorithm. This approach adapts traditional genetic operators (mutation and crossover) to the images domain and replaces the fitness function by the evaluation of the results of mutation testing. The proposed approach was validated by the execution of experiments involving eight distinct IP programs. TAIGA was able to provide up to 38.61% increase in mutation score when compared to the traditional testing for IP programs. It\'s expected that the automation of test data generation elevates the quality of image processing systems development and reduces the costs of software test activities in the images domain

The properties of natural soil are inherently variable and influence design decisions in geotechnical engineering. Apart from the inherent variability of the soil, the variability may arise due to measurement of soil properties in the field or laboratory tests and model errors. These wide ranges of variability in soil are expressed in terms of mean, variance and autocorrelation function using probability/reliability based models. The most common term used in reliability based design is the reliability index, which is a probabilistic measure of assurance of performance of structure. The main objective of the reliability based design is to quantify probability of failure/reliability of a geotechnical system considering variability in the design parameters and associated safety. In foundation design, reliability based design is useful compared to deterministic factor of safety approach. Several design codes of practice recommend the use of limit state design concept based on probabilistic models, and suggest that, development of reliability based design methodologies for practical use are of immense value. The objective of the present study is to propose reliability based design methodologies for pile foundations under static and seismic loads. The work presented in this dissertation is subdivided into two parts, namely design of pile foundations under static vertical and lateral loading; and design of piles under seismic loading, embedded in non-liquefiable and liquefiable soil. The significance of consideration of variability in soil parameters in the design of pile foundation is highlighted. A brief review of literature is presented in Chapter 2 on current pile design methods under vertical, lateral and seismic loads. It also identifies the scope of the work. Chapter 3 discusses the methods of analysis which are subsequently used for the present study. Chapter 4 presents the reliability based design methodology for vertically and laterally loaded piles based on cone penetration test data for cohesive soil. CPT data from Konaseema area in India is used for analysis. Ultimate limit sate and serviceability limit state are considered for reliability based design using CPT data and load displacement curves. Chapter 5 presents the load resistance factor design (LRFD) of vertically and laterally loaded piles based on load test data. Reliability based code calibrated partial factors are determined considering bias in failure criteria, model bias and variability in load and resistance. Chapter 6 illustrates a comprehensive study on the effect of soil spatial variability on response of vertically and laterally loaded pile foundations in undrained clay. Two-dimensional finite difference program, FLAC2D (Itasca 2005) is used to model the soil and pile. The response of pile foundations due to the effect of variance and spatial correlation of undrained shear strength is studied using Monte Carlo simulation. The influence of spatial variability on the propagation and formation of failure near the pile foundation is also examined. Chapter 7 describes reliability based design methodology of piles in non-liquefiable soil. The seismic load on pile foundation is determined from code specified elastic design response spectrum using pseudo-static approach. Variability in seismic load and soil undrained shear strength are incorporated. The effects of soil relative densities, pile diameters, earthquake predominant frequencies and peak acceleration values on the two plausible failure mechanisms; bending and buckling are examined in Chapter 8. The two-dimensional finite difference analysis is used for dynamic analysis. A probabilistic approach is proposed to identify governing failure modes of piles in liquefiable soil in Chapter 9. The variability in the soil parameters namely SPT-N value, friction angle, shear modulus, bulk modulus, permeability and shear strain at 50% of modulus ratio is considered. Monte Carlo simulation is used to determine the probability of failure. A well documented case of the failed pile of Showa Bridge in 1964 Niigata earthquake is considered as case example. Based on the studies reported in this dissertation, it can be concluded that the reliability based design of pile foundations considering variability and spatial correlation of soil enables a rational choice of design loads. The variability in the seismic design load and soil shear strength can quantify the risk involved for pile design in a rational basis. The identification of depth of liquefiable soil layer is found to be most important to identify failure mechanisms of piles in liquefiable soil. Considerations of soil type, earthquake intensity, predominant frequency of earthquake, pile material, variability of soil are also significant.

Big amounts of test data generated by the current used software testing tools (QA-C/QA-C++ and Cantata) contain a variety of different values. The variances cause enormous challenges in data aggregation and interpretation that directly affect generation of test metrics. Due to the circumstance of data processing, this master thesis introduces a database-centric test management framework for test metrics aims at centrally handling the big data as well as facilitating the generation of test metrics. Each test result will be individually parsed to be a particular format before being stored in a centralized database. A friendly front-end user interface is connected and synchronized with the database that allows authorized users to interact with the stored data. With a granularity tracking mechanism, any stored data will be systematically located and programmatically interpreted by a test metrics generator to create various kinds of high-quality test metrics. The automatization of the framework is driven by Jenkins CI to automatically and periodically performing the sequential operations. The technology greatly and effectively optimizes and reduces effort in the development, as well as enhance the performance of the software testing processes. In this research, the framework is only started at managing the testing processes on software-unit level. However, because of the independence of the database from levels of software testing, it could also be expanded to support software development at any level.

Στην εργασία, αυτή, εξετάζονται οι μέθοδοι συμπίεσης του συνόλου δοκιμής με τη χρήση πινάκων που έχουν ήδη προταθεί και προτείνεται μία νέα μέθοδος συμπίεσης δεδομένων δοκιμής για πυρήνες που ο έλεγχος ορθής λειτουργίας υλοποιείται μέσω μονοπατιών ολίσθησης. Η νέα μέθοδος επαναχρησιμοποιεί μπλοκ του πίνακα για τη σύνθεση διανυσμάτων δοκιμής. Δύο νέοι αλγόριθμοι παρουσιάζονται για επιλεκτική και πλήρη καταχώρηση τμημάτων του συνόλου δοκιμής σε πίνακα. Η προτεινόμενη μέθοδος συγκρίνεται με τις υπάρχουσες μεθόδους ως προς το ποσοστό συμπίεσης αλλά και ως προς το κόστος υλοποίησης. Για την αξιολόγηση της μεθόδου λαμβάνονται υπόψη σύνολα δοκιμής που έχουν παραχθεί για την ανίχνευση απλών σφαλμάτων μόνιμης τιμής, απλών σφαλμάτων μόνιμης τιμής με πολλαπλότητα ανίχνευσης Ν (Ν-detect) και σφαλμάτων καθυστέρησης μετάβασης. / In this work we refer to dictionary based test data compression methods. At first the already known dictionary based test data compression methods are comparably presented. Then we propose a new method and we show that the test data compression achieved by a dictionary based method can be improved significantly by suitably reusing parts of the dictionary entries. To this end two new algorithms are proposed, suitable for partial and complete dictionary coding respectively. For the evaluation of the proposed method, test sets have been generated and used based on the stuck-at fault model for single and N detection of each fault as well as on the transition fault model.

Veículo autônomo inteligente (ou apenas veículo autônomo VA) é um tipo de sistema embarcado que integra componentes físicos (hardware) e computacionais (software). Sua principal característica é a capacidade de locomoção e de operação de modo semi ou completamente autônomo. A autonomia cresce com a capacidade de percepção e de deslocamento no ambiente, robustez e capacidade de resolver e executar tarefas lidando com as mais diversas situações (inteligência). Veículos autônomos representam um tópico de pesquisa importante e que tem impacto direto na sociedade. No entanto, à medida que esse campo avança alguns problemas secundários aparecem como, por exemplo, como saber se esses sistemas foram suficientemente testados. Uma das fases do teste de um VA é o teste de campo, em que o veículo é levado para um ambiente pouco controlado e deve executar livremente a missão para a qual foi programado. Ele é geralmente utilizado para garantir que os veículos autônomos mostrem o comportamento desejado, mas nenhuma informação sobre a estrutura do código é utilizada. Pode ocorrer que o veículo (hardware e software) passou no teste de campo, mas trechos importantes do código nunca tenham sido executados. Durante o teste de campo, os dados de entrada são coletados em logs que podem ser posteriormente analisados para avaliar os resultados do teste e para realizar outros tipos de teste offline. Esta tese apresenta um conjunto de propostas para apoiar a análise do teste de campo do ponto de vista do teste estrutural. A abordagem é composta por um modelo de classes no contexto do teste de campo, uma ferramenta que implementa esse modelo e um algoritmo genético para geração de dados de teste. Apresenta também heurísticas para reduzir o conjunto de dados contidos em um log sem diminuir substancialmente a cobertura obtida e estratégias de combinação e mutação que são usadas no algoritmo. Estudos de caso foram conduzidos para avaliar as heurísticas e estratégias e são também apresentados e discutidos. / Intelligent autonomous vehicle (or just autonomous vehicle - AV) is a type of embedded system that integrates physical (hardware) and computational (software) components. Its main feature is the ability to move and operate partially or fully autonomously. Autonomy grows with the ability to perceive and move within the environment, robustness and ability to solve and perform tasks dealing with different situations (intelligence). Autonomous vehicles represent an important research topic that has a direct impact on society. However, as this field progresses some secondary problems arise, such as how to know if these systems have been sufficiently tested. One of the testing phases of an AV is the field testing, where the vehicle is taken to a controlled environment and it should execute the mission for which it was programed freely. It is generally used to ensure that autonomous vehicles show the intended behavior, but it usually does not take into consideration the code structure. The vehicle (hardware and software) could pass the field testing, but important parts of the code may never have been executed. During the field testing, the input data are collected in logs that can be further analyzed to evaluate the test results and to perform other types of offline tests. This thesis presents a set of proposals to support the analysis of field testing from the point of view of the structural testing. The approach is composed of a class model in the context of the field testing, a tool that implements this model and a genetic algorithm to generate test data. It also shows heuristics to reduce the data set contained in a log without reducing substantially the coverage obtained and combination and mutation strategies that are used in the algorithm. Case studies have been conducted to evaluate the heuristics and strategies, and are also presented and discussed.