Ranadive, P., Vaidya, V., and Rajguru, C., "Reducing Defects in Automotive Software Using Static Analysis," SAE Technical Paper 2015-01-0191, 2015, doi:10.4271/2015-01-0191.
Improving reliability and quality of software is a major aspect in automotive industry. Software reliability and quality improves by reducing bugs or defects in the software. However, finding these defects at an early stage in the software development life cycle is important to reduce rework and cost. Manually detecting defects or bugs in large code sets is time consuming and is less accurate. Hence, using static or dynamic analysis tools has become a standard practice in automotive industry. Though many such tools are commercially available, it is observed that these tools are less used for various reasons. Some of the major reasons are users need to spend considerable amount of time to learn to use these tools to get desired output reports, customized checks are required for an application that are not provided by the tool and reports are too lengthy as well as cumbersome to analyze. In this paper, we propose a static analysis based YUCCA-CCC tool that addresses some of the major limitations like support for differential code sets, function pointers analysis, and customized checks. We treat this tool as a complementary tool to other tools since it incorporates customized checks and customized report formats. We also discuss our results obtained on three automotive software source codes in production. The results show 42.30% increase in accuracy over the manual process.