Une des étapes les plus cruciales dans le processus d’analyse des performances d’une application est la détection des goulets d’étranglement. Un goulet étant tout évènement qui contribue à l’allongement temps d’exécution, la détection de ses causes est importante pour les développeurs d’applications afin de comprendre les défauts de conception et de génération de code. Cependant, la détection de goulets devient un art difficile. Dans le passé, des techniques qui reposaient sur le comptage du nombre d’évènements, arrivaient facilement à trouver les goulets. Maintenant, la complexité accrue des micro-architectures modernes et l’introduction de plusieurs niveaux de parallélisme ont rendu ces techniques beaucoup moins efficaces. Par conséquent, il y a un réel besoin de réflexion sur de nouvelles approches.Notre travail porte sur le développement d’outils d’évaluation de performance des boucles de calculs issues d’applications scientifiques. Nous travaillons sur Decan, un outil d’analyse de performance qui présente une approche intéressante et prometteuse appelée l’Analyse Décrémentale. Decan repose sur l’idée d’effectuer des changements contrôlés sur les boucles du programme et de comparer la version obtenue (appelée variante) avec la version originale, permettant ainsi de détecter la présence ou pas de goulets d’étranglement.Tout d’abord, nous avons enrichi Decan avec de nouvelles variantes, que nous avons conçues, testées et validées. Ces variantes sont, par la suite, intégrées dans une analyse de performance poussée appelée l’Analyse Différentielle. Nous avons intégré l’outil et l’analyse dans une méthodologie d’analyse de performance plus globale appelée Pamda.Nous décrirons aussi les différents apports à l’outil Decan. Sont particulièrement détaillées les techniques de préservation des structures de contrôle du programme,ainsi que l’ajout du support pour les programmes parallèles.Finalement, nous effectuons une étude statistique qui permet de vérifier la possibilité d’utiliser des compteurs d’évènements, autres que le temps d’exécution, comme métriques de comparaison entre les variantes Decan / A crucial step in the process of application performance analysis is the accurate detection of program bottlenecks. A bottleneck is any event which contributes to extend the execution time. Determining their cause is important for application developpers as it enable them to detect code design and generation flaws.Bottleneck detection is becoming a difficult art. Techniques such as event counts,which succeeded to find bottlenecks easily in the past, became less efficient because of the increasing complexity of modern micro-processors, and because of the introduction of parallelism at several levels. Consequently, a real need for new analysis approaches is present in order to face these challenges.Our work focuses on performance analysis and bottleneck detection of computeintensive loops in scientific applications. We work on Decan, a performance analysis and bottleneck detection tool, which offers an interesting and promising approach called Decremental Analysis. The tool, which operates at binary level, is based on the idea of performing controlled modifications on the instructions of a loop, and comparing the new version (called variant) to the original one. The goal is to assess the cost of specific events, and thus the existence or not of bottlenecks.Our first contribution, consists of extending Decan with new variants that we designed, tested and validated. Based on these variants, we developed analysis methods which we used to characterize hot loops and find their bottlenecks. Welater, integrated the tool into a performance analysis methodology (Pamda) which coordinates several analysis tools in order to achieve a more efficient application performance analysis.Second, we introduce several improvements on the Decan tool. Techniquesdeveloped to preserve the control flow of the modified programs, allowed to use thetool on real applications instead of extracted kernels. Support for parallel programs(thread and process based) was also added. Finally, our tool primarily relying on execution time as the main concern for its analysis process, we study the opportunity of also using other hardware generated events, through a study of their stability, precision and overhead

Cílem této magisterské práce je navrhnout a implementovat IDE (integrované vývojové prostředí), které zvýší efektivitu a bezpečnost vývoje pro SIMATIC WinCC Open Architecture. Tato práce je založena na výzkumu provedeném týmem z Technické univerzity v Eindhovenu a splňuje požadavky pocházející ze SCD sekce v CERN (Evropské organizace pro jaderný výzkum). Vyvinuté IDE je postaveno na platformě Eclipse, přičemž pro syntaktickou analýzu, linkování a sémantickou analýzu kódu používá Xtext framework. IDE nabízí také podporu pro nově vytvořený programovací jazyk, který umožňuje programátorům jednoduše nadefinovat šablonu pro konfigurační soubory používané WinCC OA. Interpret tohoto nového jazyka je schopen provést syntaktickou analýzu šablony a konfiguračního souboru a rozhodnout, zdali konfigurační soubor odpovídá šabloně. Praktickým výstupem této práce je integrované vývojové prostředí, které podporuje vývoj WinCC OA aplikací v CERN a periodicky provádí analýzu kódu těchto aplikací napsaného v jazyce Control script.

Aim Develop an IT artifact that provides formative feedback for students based on their programming assignments. Background One of the best methods to learn programming is by practice. Providing feedback to students is an important and a valuable factor for improving learning, which plays a vital part in the student’s possibility to enhance and improve its solutions. Software development courses have several assignments and each course instructs about 100 students. To assess and provide feedback for all the students and each assignment demands considerable resources. In a survey conducted by TCO (2013) half of the respondents’ state that feedback is rarely or never given in reasonable time. Method Action Design Research (ADR) was used to intervene an organizational problem in parallel with building and evaluating an IT artifact. Conclusion The results from the study were four generated design principles and a proposed solution on how to use existing static code analysis tools for provide formative feedback to students.

<p dir="ltr">The research focuses on integrating automation, specifically security and Infrastructure as Code (IaC), into the Software Development Life Cycle (SDLC). This integration aims to enhance the efficiency, quality, and security of software development processes. The study explores the benefits and challenges associated with implementing DevSecOps practices, which combine development, security, and operations into a unified process.</p><h3>Background and Motivation</h3><p dir="ltr">The rise of new technologies and increasing demand for high-quality software have made software development a crucial aspect of business operations. The SDLC is essential for ensuring that software meets user requirements and maintains high standards of quality and security. Security, in particular, has become a critical focus due to the growing threat of cyber-attacks and data breaches. By integrating security measures early in the development process, companies can better protect their software and data.</p><h3>Objectives</h3><p dir="ltr">The primary objectives of this research are:</p><ol><li><b>Examine the Benefits and Challenges</b>: To investigate the advantages and difficulties of integrating DevSecOps and IaC within the SDLC.</li><li><b>Analyze Impact on Security and Quality</b>: To assess how automation affects the security and quality of software developed through the SDLC.</li><li><b>Develop a Framework</b>: To create a comprehensive framework for integrating DevSecOps and IaC into the SDLC, thereby improving security and reducing time to market.</li></ol><h3>Methodology</h3><p dir="ltr">The research employs a mixed-methods approach, combining qualitative and quantitative methods:</p><ul><li><b>Qualitative</b>: A literature review of existing research on DevSecOps, IaC, and SDLC, providing a theoretical foundation and context.</li><li><b>Quantitative</b>: Building a CI/CD (Continuous Integration/Continuous Deployment) pipeline from scratch to collect empirical data. This pipeline serves as a case study to gather insights into how automation impacts software security and quality.</li></ul><h3>Tools and Technologies</h3><p dir="ltr">The study utilizes various tools, including:</p><ul><li><b>GitHub</b>: For version control and code repository management.</li><li><b>Jenkins</b>: To automate the CI/CD pipeline, including building, testing, and deploying applications.</li><li><b>SonarQube</b>: For static code analysis, detecting code quality issues, and security vulnerabilities.</li><li><b>Amazon Q</b>: An AI-driven tool used for code generation and security scanning.</li><li><b>OWASP Dependency-Check</b>: To identify vulnerabilities in project dependencies.</li><li><b>Prometheus and Grafana</b>: For monitoring and collecting metrics.</li><li><b>Terraform</b>: For defining and deploying infrastructure components as code.</li></ul><h3>Key Findings</h3><ul><li><b>Reduction in Defect Density</b>: Automation significantly reduced defect density, indicating fewer bugs and higher code quality.</li><li><b>Increase in Code Coverage</b>: More comprehensive testing, leading to improved software reliability.</li><li><b>Reduction in MTTR, MTTD, and MTTF</b>: Enhanced system reliability and efficiency, with faster detection and resolution of issues.</li><li><b>Improved System Performance</b>: Better performance metrics, such as reduced response time and increased throughput.</li></ul><h3>Conclusion</h3><p dir="ltr">The study concludes that integrating security and IaC automation into the SDLC is crucial for improving software quality, security, and development efficiency. However, despite the clear benefits, many companies are hesitant to adopt these practices due to perceived challenges, such as the upfront investment, complexity of implementation, and concerns about ROI (Return on Investment). The research underscores the need for continued innovation and adaptation in software development practices to meet the evolving demands of the technological landscape.</p><h3>Areas for Further Research</h3><p dir="ltr">Future studies could explore the broader impact of automation on developer productivity, job satisfaction, and long-term security practices. There is also potential for developing advanced security analysis techniques using machine learning and artificial intelligence, as well as investigating the integration of security and compliance practices within automated SDLC frameworks.</p>