Assessment of code and design quality using Software Code Assessment tools is important for continuous improvement and monitoring of code quality in the software industry in general and for global software development in particular. Static Analysis is believed to help identify issues at an early stage in the software development life cycle (SDLC), however it is still under utilized in the industry. In this paper, we discuss our experiences in determining the importance of Static Analysis and the extent to which the defects (that could otherwise be slipped to later stages of SDLC) could have been reduced with the continuous use of Static Analysis. Towards this end, we have performed analysis of defects reported from testing/field by correlating the defects to Static Analysis Rules, for projects that were developed across different regions. Our focus was to gather insight into the number and type of defects that could be identified in advance through Static Analysis Rules. The purpose of gaining the insights is to justify the ROI for software quality checks, fine tune, update existing software quality practices and introduce new practices uniformly across the teams as necessary. The results substantiate the role of Static Analysis in reducing technical debt by identifying potential defects early in the SDLC.

Most of the software code quality assessment and monitoring methods uses Quality Model (QM) as an aid to capture quality requirements of the software. An important aspect concerning use of QM is classification of Program Analysis (PA) rules into QM according to their relevance to quality attributes such as maintainability, reliability etc. Currently such classification is performed manually by experts and most of the PA tools (such as FxCop for C#, FindBugs for Java, PC-Lint for C/C++) support hundreds of PA rules. Hence performing classification manually can be very effort intensive and time consuming and can lead to concerns like subjectivity and inconsistency. Hence we propose a light weight semiautomated method to expedite classification and make classification activity less effort intensive. Proposed classifier is based on natural language processing (NLP) techniques and uses a keyword matching algorithm. We have computed precision and recall for such a classifier. We have also shown results from applying technique on classifying rules from FxCop, PC-Lint, and FindBugs into the EMISQ QM. We believe that proposed approach will significantly help in reducing the time required to perform classification and hence also to incorporate newer PA tools and rules into QM based methods.

Siemens, Corporate Technology, Development Center, Asia Australia (CT DC AA) has been developing and maintaining several software projects for the Industry, Energy, Healthcare, and Infrastructure & Cities sectors of Siemens. The critical nature of these projects necessitates a high level of software code quality. As part of the code quality program at CT DC AA the strategy is to have a scalable method towards identification of issues affecting code quality of projects across the organization. Traditionally, code quality experts in Siemens used EMISQ method to assess code quality. EMISQ requires about three person months (two experts for six weeks) for 50-100 kLoC, making it effort intensive and time consuming. Thus, scaling this assessment method to include the hundreds of projects in CT DC AA poses many challenges. To address this, we have developed a lightweight assessment method called SCQAM (Structured Code Quality Assessment Method). SCQAM is an expert-based method wherein manual assessment of code quality by experts is directed by the systematic application of code analysis tools. In this paper, we describe the SCQAM method, experiences in applying it to projects in CT DC AA, challenges faced and initiatives taken to enable fixing of systemic issues reported by assessments. The insights from our SCQAM experience can provide useful pointers to other organizations and practitioners interested in assessment and improvement of software code quality.

Siemens Corporate Development Center Asia Australia (CT DC AA) develops and maintains software applications for the Industry, Energy, Healthcare, and Infrastructure & Cities sectors of Siemens. The critical nature of these applications necessitates a high level of software design quality. A survey of software architects indicated a low level of satisfaction with existing design assessment practices in CT DC AA and highlighted several shortcomings of existing practices. To address this, we have developed a design assessment method called MIDAS (Method for Intensive Design ASsessments). MIDAS is an expert-based method wherein manual assessment of design quality by experts is directed by the systematic application of design analysis tools through the use of a three view-model consisting of design principles, project-specific constraints, and an “ility”-based quality model. In this paper, we describe the motivation for MIDAS, its design, and its application to three projects in CT DC AA. We believe that the insights from our MIDAS experience not only provide useful pointers to other organizations and practitioners looking to assess and improve software design quality but also suggest research questions for the software engineering community to explore.