Inbyggda system finns i nästan alla enheter som används i vårt dagliga liv, som exempelvis mobiltelefoner, kylskåp och bilar. En del enheter kan vara betydligt känsligare än andra, vilket innebär att en bugg som existerar i ett system kan orsaka skada, till och med förlust av människoliv, eller orsakar ingen skada alls. Mjukvarutestning och mjukvarufelsökning genomförs för att reducera buggar i ett system.Utbildningsprogrammet Datateknik och Mobil IT på Malmö universitet fokuserar inte på att undervisa mjukvarufelsökning med hjälp av felsökningsverktyg. Således presenterar denna forskning en felsökningslaboration skapat för studenter som går Datateknik och Mobil IT, som anses hjälpa studenterna att få kunskap i hur man använder felsökningsverktyget SAM4E Xplained Pro för att lokalisera buggar. Som ett resultat, utfördes felsökningslaborationen av fyra studenter varav 75 procent av buggarna hittades och åtgärdades. / Embedded systems are found in almost every device used in our daily lives, including cell phones, refrigerators, and cars. Some devices may be significantly more sensitive than others, meaning a bug appearing in a system could cause harm, even loss of human lives or cause no harm at all. To reduce bugs in a system, software testing and software debugging are performed.The Computer Science program at Malmö University does not focus on teaching software debugging using a debugger. Thus, this thesis presents a debugging lab created for Computer Science students, considered to help them gain knowledge in how to use the debugger SAM4E Xplained Pro to locate bugs. As a result, four students performed the debugging lab of which 75 percent of the bugs were found and remedied.

The present study is concerned with developing some statistical models to evaluate and analyze software reliability. We have developed the analytical structure of the logistic model to be used for testing and evaluating the reliability of a software package. The proposed model has been shown to be useful in the testing and debugging stages of the developmental process of a software package. It is important that prior to releasing a software package to marketing that we have achieved a target reliability with an acceptable degree of confidence. The proposed model has been evaluated and compared with several existing statistical models that are commonly used. Real software failure data was used for the comparison of the proposed logistic model with the others. The proposed model gives better results or it is equally effective. The logistic model was also used to model the mean time between failure of software packages. Real failure data was used to illustrate the usefulness of the proposed statistical procedures. Using the logistic model to characterize software failures we proceed to develop Bayesian analysis of the subject model. This modeling was based on two different difference equations whose parameters were estimated with Bayesian regressions subject to specific prior and mean square loss function.