This work addresses the issues concerned with the provision of scalable video services over heterogeneous networks particularly with regards to dynamic adaptation and user’s acceptable quality of service. In order to provide and sustain an adaptive and network friendly multimedia communication service, a suite of techniques that achieved automatic scalability and adaptation are developed. These techniques are evaluated objectively and subjectively to assess the Quality of Service (QoS) provided to diverse users with variable constraints and dynamic resources. The research ensured the consideration of various levels of user acceptable QoS The techniques are further evaluated with view to establish their performance against state of the art scalable and non-scalable techniques. To further improve the adaptability of the designed techniques, several experiments and real time simulations are conducted with the aim of determining the optimum performance with various coding parameters and scenarios. The coding parameters and scenarios are evaluated and analyzed to determine their performance using various types of video content and formats. Several algorithms are developed to provide a dynamic adaptation of coding tools and parameters to specific video content type, format and bandwidth of transmission. Due to the nature of heterogeneous networks where channel conditions, terminals, users capabilities and preferences etc are unpredictably changing, hence limiting the adaptability of a specific technique adopted, a Dynamic Scalability Decision Making Algorithm (SADMA) is developed. The algorithm autonomously selects one of the designed scalability techniques basing its decision on the monitored and reported channel conditions. Experiments were conducted using a purpose-built heterogeneous network simulator and the network-aware selection of the scalability techniques is based on real time simulation results. A technique with a minimum delay, low bit-rate, low frame rate and low quality is adopted as a reactive measure to a predicted bad channel condition. If the use of the techniques is not favoured due to deteriorating channel conditions reported, a reduced layered stream or base layer is used. If the network status does not allow the use of the base layer, then the stream uses parameter identifiers with high efficiency to improve the scalability and adaptation of the video service. To further improve the flexibility and efficiency of the algorithm, a dynamic de-blocking filter and lambda value selection are analyzed and introduced in the algorithm. Various methods, interfaces and algorithms are defined for transcoding from one technique to another and extracting sub-streams when the network conditions do not allow for the transmission of the entire bit-stream.

Masters of Science / Coders of databases repeatedly face the problem of checking their Structured Query Language (SQL) code. Instructors face the difficulty of checking student projects and lab assignments in database courses. We collect and categorize common MySQL programming errors into three groups: data definition errors, data manipulation errors, and transaction control errors. We build these into a comprehensive list of MySQL errors, which novices are inclined make during database programming. We collected our list of common MySQL errors both from the technical literature and directly by noting errors made in assignments handed in by students. In the results section of this research, we check and summarize occurrences of these errors based on three characteristics as semantics, syntax, and logic. These data form the basis of a future static MySQL checker that will eventually assist database coders to correct their code automatically. These errors also form a useful checklist to guide students away from the mistakes that they are prone to make.

We discuss a new approach to the construction of software systems. Instead of attempting to build a system that is as free of errors as possible, the designer instead identifies key properties that the execution must satisfy to be acceptable to its users. Together, these properties define the acceptability envelope of the system: the region that it must stay within to remain acceptable. The developer then augments the system with a layered set of components, each of which enforces one of the acceptability properties. The potential advantages of this approach include more flexible, resilient systems that recover from errors and behave acceptably across a wide range of operating environments, an appropriately prioritized investment of engineering resources, and the ability to productively incorporate unreliable components into the final software system. / Singapore-MIT Alliance (SMA)

Denna studie undersöker användningen av kodassistenter som bygger på generativ AI (GAI) för systemutveckling och testning inom myndigheter och större företag, med fokus på säkerhetsrisker och funktionella krav. Detta görs genom en kartläggning där intervjuer och interna såväl som publika dokument används som datainsamlingsmetod. Med den snabba utvecklingen av stora språkmodeller (LLM:s) och deras tillämpningar inom systemutveckling är det viktigt för organisationer att göra val av AI-verktyg som uppfyller både säkerhets- och funktionskrav. Studien kartlägger och jämför olika verktyg baserat på dessa kriterier, med syfte att hjälpa organisationer att göra välavvägda val i ett ständigt växande utbud av GAIverktyg för programmering. Säkerhetsriskerna som identifierats inkluderar risken för informationsläckor och generering av osäker kod. Studien föreslår mitigerande åtgärder som användning av verktyg som inte sparar eller tränar på användardata för att minska dessa risker. Funktionella krav som identifierats inkluderar förmågan att generera och förklara kod, samt integration med utvecklingsmiljöer som Visual Studio och VisualStudio Code. Verktygen filtrerades därefter baserat på de identifierade mitigeringarna, vilket resulterade i en rekommendation av sex verktyg som uppfyller säkerhetskraven. Fyra av dessa sex verktyg uppfyller i sin tur de ställda kriterierna från samarbetspartnern (Trafikverket) gällande funktionskrav. Denna studie bidrar till den akademiska forskningen genom att erbjuda en ökad förståelse för säkerhets- och funktionsaspekterna av GAI-verktyg (ChatGPT, Claude, Codeium, Codiumate, Cody, GitHub Copilot, Gemini, Q, Tabnine) inom systemutveckling och testning. Detta genom en lista över potentiella säkerhetsrisker och en interaktiv jämförelsetabell där identifierade GAI-verktygkan filtreras efter olika krav. / This study examines the use of generative AI (GAI) coding assistants for system development and testing within government agencies and big companies, focusing on security risks and functional requirements. This is done through a survey where interviews and both internal and public documents are used as datacollection methods. With the rapid development of large language models (LLMs) and their growing use in system development, it is crucial for organizations to choose AI tools that     meet both security and functionality requirements. This survey compares various tools based on these criteria, aiming to assist organizations in making wellbalanced choices in an ever-growing range of GAI tools for coding. The security risks identified include the risk of information leaks and the generation of insecure code. The study suggests mitigations such as the use of tools that do not save or train on user data to reduce these risks. Functional requirements identified include the ability to generate and explain code, as well as integration with popular development environments like Visual Studio and Visual Studio Code. The tools were then filtered based on the identified mitigations, resulting in a recommendation of six tools that meet the security requirements. Four of these six tools, in turn, meet the set criteria from the partner (SwedishTransport Administration) regarding functional requirements. This study contributes to academic research by offering an increased understanding of the security and functionality aspects of GAI tools (ChatGPT, Claude, Codeium, Codiumate, Cody, GitHub Copilot, Gemini, Q, Tabnine) within system development and testing. This through a list of potential security risks and an interactive comparison table where identified GAI tools can be filtered according to different requirements.