Why is dynamic analysis not used as extensively as static analysis: an industrial study

Abstract

Code Assessments using static and dynamic analyses are important for the maintenance of code quality of software in the industry. These analyses, though understood to be beneficial, have several practical limitations. The intent of our study was to understand the usage of these analyses across various teams in Siemens during code assessments, reasons for the success of static analysis and the challenges in dynamic analysis. The evaluation was conducted through a survey of the practices of developers in different development teams. Additionally interviews were conducted with expert reviewers and their experiences documented. Our study has revealed that institutionalization of code quality initiatives and customized toolkit support for static a n a l y s i s a r e s o m e o f t h e k e y r e a s o n s f o r t h e s u c c e s s f u l implementation of static analysis. Some of the problems hindering the widespread adoption of dynamic analysis include the direct dependency with test cases, scalability, and understandability of the results. Various implications of the results are discussed, including the possible improvements that could increase the acceptance of dynamic analysis on a regular basis.

Efficient and Scalable Collection of Dynamic Metrics Using MapReduce

“…One of the major issues is to extract relevant metrics from large amount of data generated during software execution [17]. The processing of this much data takes time and thus creates delay in getting the final metric values.…”