High quality software is a key component of various technology systems that are crucial to software producers, users, and society in general. Software application development today uses software from external sources, to achieve software implementation goals. Numerous methods, activities, and standards have been developed in order to realize quality software. Nevertheless, the pursuit for new methods of realizing and assuring quality in software is incessant. Researchers in the software engineering field are in pursuit of methods that can be on par with changing technology. Assessment of open-source software can be supported by a methodology that uses data from prior releases of a software product to predict the quality of a future release. The proposed methodology is validated using a case study of MPICH ? an open-source software product from the field of high-performance computing. A quantitative model and a module-order model have been developed that can predict the modules that are expected to have code-churn and the amount of code-churn in each module. Code-churn is defined as the amount of update activity that has been done to a software product in order to fix bugs. Further validation of the proposed methodology on other software and development of classification models for the quality factor code-churn are recommended as future work.

A mature code-base of 1 300 000 LOC for a period of 20 months has been examined. This paper investigates if churn is a significant factor in finding refactoring candidates. In addition it looks at the variables Lines of Code (LOC), Technical Debt (TD), Duplicated lines and Complexity to find out if any of these indicators can inform a coder as to what to refactor. The result is that churn is the strongest variable out of the studied variables followed by LOC and TD. / En kodbas på 1 300 000 rader kod har undersökts under 20 månader. Denna uppsats undersöker om kodens användningsfrekvens är en signifikant faktor för att finna refaktoreringskandidater. Uppsatsen tittar även antal kodrader, teknisk skuld, antal duplicerade kodrader och komplexitet för att undersöka om dessa indikatorer kan informera en programmerare om vad som ska refaktoreras. Resultatet är att kodens användningsfrekvens är den starkaste variabeln följt av antal kodrader samt teknisk skuld.