Software Complexity and Project Performance

Abstract

Software complexity is the all-embracing notion referring to factors that decide the level of difficulty in developing software projects. In this master thesis we have examined the concept VRIWZDUH FRPSOH[LW and its effect on software projects, concerning productivity and quality, for our assignor, Ericsson Mobile Data Design AB (ERV). Our literature studies have compelled to the development of our own model of complexity, as no such comprehensive model existed. With this model as a starting point we have examined existing methods of different software metrics. According to our model of complexity the metrics we found focused either on software quality or software size/effort. In our suggestion to ERV, of how they should measure their projects, we have combined both of these attributes in a metric called performance. Two of the functional size methods we found, Function Point Analysis (FPA) and Full Function Points (FFP), were applied on a completed software project to determine which method suited best for the projects at ERV. Our hypothesis was that FFP was most suitable and our tests of FFP and FPA proved that this hypothesis was right. Based on our theoretical studies of the quality metrics we suggested that a structural complexity measure (or a combination of several of them) and error density should each be combined with FFP, at different stages of the development process, to present the measure of performance.