This thesis presents and evaluates how to select the data to be logged in an embedded realtime system so as to be able to give confidence that it is possible to perform an accurate identification of the fault(s) that caused any runtime errors. Several log-selection strategies were evaluated by injecting random faults into a simulated real-time system. An instrument was created to perform accurate detection and identification of these faults by evaluating log data. The instrument’s output was compared to ground truth to determine the accuracy of the instrument. Three strategies for selecting the log entries to keep in limited permanent memory were created. The strategies were evaluated using log data from the simulated real-time system. One of the log-selection strategies performed much better than the other two: it minimized processing time and stored the maximum amount of useful log data in the available storage space. / Denna uppsats illustrerar hur det blev fastställt vad som ska loggas i ett inbäddat realtidssystem för att kunna ge förtroende för att det är möjligt att utföra en korrekt identifiering av fel(en) som orsakat körningsfel. Ett antal strategier utvärderades för loggval genom att injicera slumpmässiga fel i ett simulerat realtidssystem. Ett instrument konstruerades för att utföra en korrekt upptäckt och identifiering av dessa fel genom att utvärdera loggdata. Instrumentets utdata jämfördes med ett kontrollvärde för att bestämma riktigheten av instrumentet. Tre strategier skapades för att avgöra vilka loggposter som skulle behållas i det begränsade permanenta lagringsutrymmet. Strategierna utvärderades med hjälp av loggdata från det simulerade realtidssystemet. En av strategierna för val av loggdata presterade klart bättre än de andra två: den minimerade tiden för bearbetning och lagrade maximal mängd användbar loggdata i det permanenta lagringsutrymmet.

This thesis work describes how to apply the stochastic analysis framework, presented in [1] for general priority-driven periodic real-time systems. The proposed framework is applicable to compute the response time distribution, the worst-case response time, and the deadline miss probability of the task under analysis in the fixed-priority driven scheduling system. To be specific, we modeled the task execution time by using the beta distribution. Moreover, we have evaluated the existing stochastic framework on a wide range of periodic systems with the help of defined evaluation parameters. In addition we have refined the notations used in system model and also developed new mathematics in order to facilitate the understanding with the concept. We have also introduced new concepts to obtain and validate the exact probabilistic task response time distribution.    Another contribution of this thesis is that we have extended the existing system model in order to deal with stochastic release time of a job. Moreover, a new algorithm is developed and validated using our extended framework where the stochastic dependencies exist due to stochastic release time patterns. / This is Second Version of the report. Submitted after few modifications made on the order of Thomas Nolte (Thesis Examiner). / START - Stochastic Real-Time Analysis of Embedded Software Systems