Along with continuously increasing computerization, our expectations on software and hardware reliability increase considerably. Therefore, software reliability has become one of the most important software quality attributes. Software reliability modeling based on test data is done to estimate whether the current reliability level meets the requirements for the product. Software reliability modeling also provides possibilities to predict reliability. Costs of software developing and tests together with profit issues in relation to software reliability are one of the main objectives to software reliability prediction. Software reliability prediction currently uses different models for this purpose. Parameters have to be set in order to tune the model to fit the test data. A slightly different prediction model, Time Invariance Estimation, TIE is developed to challenge the models used today. An experiment is set up to investigate whether TIE could be found useful in a software reliability prediction context. The experiment is based on a comparison between the ordinary reliability prediction models and TIE.

The Kalman Filter has many applications in control and signal processing but may also be used to reconstruct a higher resolution image from a sequence of lower resolution images (or frames). If the sequence of low resolution frames is recorded by a moving camera or sensor, where the motion can be accurately modeled, then the Kalman filter may be used to update pixels within a higher resolution frame to achieve a more detailed result. This thesis outlines current methods of implementing this algorithm on a scene of interest and introduces possible improvements for the speed and efficiency of this method by use of block operations on the low resolution frames. The effects of noise on camera motion and various blur models are examined using experimental data to illustrate the differences between the methods discussed.

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