Software measurement process is a significant part of process improvement in software organizations. The organizations usually follow a measurement process that includes measures selection, data collection, and analysis to improve their processes. Most of the software organizations face difficulties in deciding the measures to collect since there is no universal set of measures for all types of organizations and projects. Experience shows that measurement can be more successful if the measures are collected based on the goals of the organization or the project which it will serve. A few methodologies exist to aid the software organizations. Goal Question Metric (GQM) is one of the most widely known and used one. However, one of the major constraints for the organizations is the associated cost when collecting the measures. Therefore, software organizations also require selecting the optimum set of measures which are good enough for the organization. This thesis study aims to provide solution for this problem. We propose a model, named ‗Optimum Measure Set Decision Model (OMSD)‘, which is an extension of GQM paradigm. The model is based on a heuristics approach, which aims to provide the optimum set of measures from a large number of possible measures. To develop the model, we identified the factors which are significant in selecting the optimum set of measures based on the literature survey results. Then, we evaluated those factors by conducting an empirical study. As the empirical research strategy, we used traditional fixed non-experimental design strategy. We performed a survey by distributing a structured questionnaire in order to evaluate the important factors we identified when selecting the optimum number of measures to be collected in an organization. We evaluated the heuristics rules by means of some sample cases we created. Moreover, we provided an idea for an alternative solution to optimize the number of measures to be collected for the future research.

Software measurement process is a significant part of process improvement in software organizations. The organizations usually follow a measurement process that includes measures selection, data collection, and analysis to improve their processes. Most of the software organizations face difficulties in deciding the measures to collect since there is no universal set of measures for all types of organizations and projects. Experience shows that measurement can be more successful if the measures are collected based on the goals of the organization or the project which it will serve. A few methodologies exist to aid the software organizations. Goal Question Metric (GQM) is one of the most widely known and used one. However, one of the major constraints for the organizations is the associated cost when collecting the measures. Therefore, software organizations also require selecting the optimum set of measures which are good enough for the organization. This thesis study aims to provide solution for this problem. We propose a model, named ‗Optimum Measure Set Decision Model (OMSD)‘, which is an extension of GQM paradigm. The model is based on a heuristics approach, which aims to provide the optimum set of measures from a large number of possible measures. To develop the model, we identified the factors which are significant in selecting the optimum set of measures based on the literature survey results. Then, we evaluated those factors by conducting an empirical study. As the empirical research strategy, we used traditional fixed non-experimental design strategy. We performed a survey by distributing a structured questionnaire in order to evaluate the important factors we identified when selecting the optimum number of measures to be collected in an organization. We evaluated the heuristics rules by means of some sample cases we created. Moreover, we provided an idea for an alternative solution to optimize the number of measures to be collected for the future research.