Early stage software reliability prediction using Bayesian networks

While much work has been done in estimating software reliability, little attention is paid to predict reliability as early as at the design time. In this report, we present our initial research results of building an early stage software reliability prediction model. In Part I, we will first investigate and extract essential factors related to early stage software reliability prediction. A reliability model is then proposed which incorporates software design metrics, software architecture specification, operational profiles, software development process and environment. The model is constructed in Bayesian networks to address the existence of uncertainty underlying the available design time information. In Part II, we will validate a small portion of the proposed reliability model. We first obtain various metrics values and test results on an object oriented student project. A small model is then generated by a specific algorithm, and various experiments are done on it. The prediction results are analyzed

Justified test foci definition an empirical approach

Since complete testing is not possible, testers have to focus their effort on those parts of the software which they expect to have defects, the test foci. Despite the crucial importance of a systematic and justified definition of the test foci, this task is not well established in practice. Usually, testing resources are uniformly distributed among all parts of the software. A risk of this approach is that parts which contain defects are not sufficiently tested, whereas areas that do not contain defects attain too much consideration. In this thesis, a systematic approach is introduced that allows testers to make justified decisions on the test foci. For this purpose, structural as well as historical characteristics of the software’s past releases are analysed visually and statistically in order to find indicators for the software’s defects. Structural characteristics refer to the internal structure of the software. This thesis concentrates on the analysis of bad software characteristics, also known as “bad smells”. Historical characteristics considered in this thesis are the software’s change history and the software’s age. Simple and combined analyses of defect variance are introduced in order to determine indicators for defects in software. For this purpose, the defect variance analysis diagram is used to explore the relationship between the software’s characteristics and its faultiness visually. Then, statistical procedures are applied in order to determine whether the results obtained visually are statistically significant. The approach is validated in the context of open source development as well as in an industrial setting. For this purpose, seven open source programs as well as several releases of a commercial program are analysed. Thus, the thesis increases the empirical body of knowledge concerning the empirical validation of indicators for defects in software. The results show that there is a subset of bad smells that are well suited as indicators for defects in software. A good indicator in most of all analysed programs is the “God Class” bad smell. Among the historical characteristics analysed in the industrial context, the number of distinct authors as well as the number of changes performed to a file proved to be useful indicators for defects in software

Reliability of a commercial telecommunications system

International audienceWe analyze data collected on a commercial telecommunications system and summarize some of the lessons learned from this study. The data correspond to failure and fault information recorded during system validation and operation: 3063 trouble reports corresponding to a five year period during which 5 versions of the system have been developed and more than one hundred systems have been introduced in the field. The failure information includes software failures as well as hardware failures due to design faults