Managing Software Process Improvement (SPI) through Statistical Process Control (SPC)

Measurement based software process improvement is nowadays a mandatory activity. This implies continuous process monitoring in order to predict its behavior, highlight its performance variations and, if necessary, quickly react to them. Process variations are due to common causes or assignable ones. The former are part of the process itself while the latter are due to exceptional events that result in an unstable process behavior and thus in less predictability. Statistical Process Control (SPC) is a statistical based approach able to determine whether a process is stable or not by discriminating between the presence of common cause variation and assignable cause variation. It is a well-established technique, which has shown to be effective in manufacturing processes but not yet in software process contexts. Here experience in using SPC is not mature yet. Therefore a clear understanding of the SPC outcomes still lacks. Although many authors have used it in software, they have not considered the primary differences between manufacturing and software process characteristics. Due to such differences the authors sustain that SPC cannot be adopted “as is” but must be tailored. In this sense, we propose an SPC-based approach that reinterprets SPC, and applies it from a Software Process point of view. The paper validates the approach on industrial project data and shows how it can be successfully used as a decision support tool in software process improvement.