Code search engines for the next generation

Abstract

Given the abundance of software in open source repositories, code search engines are increasingly turning to “big data” technologies such as natural language processing and machine learning, to deliver more useful search results. However, like the syntax-based approaches traditionally used to analyze and compare code in the first generation of code search engines, big data technologies are essentially static analysis processes. When dynamic properties of software, such as run-time behavior (i.e., semantics) and performance, are among the search criteria, the exclusive use of static algorithms has a significant negative impact on the precision and recall of the search results as well as other key usability factors such as ranking quality. Therefore, to address these weaknesses and provide a more reliable and usable service, the next generation of code search engines needs to complement static code analysis techniques with equally large-scale, dynamic analysis techniques based on its execution and observation. In this paper we describe a new software platform specifically developed to achieve this by simplifying and largely automating the dynamic analysis (i.e., observation) of code at a large scale. We show how this platform can combine dynamically observed properties of code modules with static properties to improve the quality and usability of code search results.