OPAL: An extensible framework for ontology‐based program analysis

Abstract

SummaryThe syntactic information of a program can be represented as resource description framework (RDF) triples called program triples. We propose an extensible static analysis framework, called OPAL—Ontology‐based Program AnaLysis. The framework enables formal representation of external knowledge, such as usage knowledge of libraries and domain knowledge. Utilizing this knowledge and the program triples, we compute the semantic information, called static trace of the program. It is generated through path‐sensitive intraprocedural traversal of the program. We approximate information in case of loops by unrolling them a fixed number of times. The main contribution of the framework is to store the static trace as RDF triples called semantic triples. They are described using the Program Analysis ontology proposed in this article. The program triples and the semantic triples are together called consolidated program triples. These triples are stored and used to accelerate the execution of client‐analyses specified by the end user. In the framework, a client‐analysis is specified by a set of conjunctive expressions that use SPARQL (W3C RDF query language) queries. The framework is effective for the client‐analyses that warrant sound and approximate information. The effectiveness is assessed first, using two source‐code‐analyses that require only the program triples, and then 10 intraprocedural path‐sensitive analyses that require the consolidated program triples. Using NPB and SPEC 2006 benchmarks, we achieve an improvement in the conciseness of analysis specifications. Also, the execution time using OPAL is competitive to LLVM's clang for individual analysis and outperforms clang over a series of analyses because of the reuse of consolidated program triples.

Source code patches from dynamic analysis

“…Work relevant to the APG includes established general representations such as control flow graphs [1] and call graphs, systemspecific internal representations (e.g. LLVM's IR [5]), and taskspecific representations such as source-code query graphs [8,9], among others [12]. These differ from the APG in specificity (e.g.…”

KLAUS-Tr: Knowledge & learning-based unit focused arithmetic word problem solver for transfer cases

“…Ontology is a formal knowledge modeling framework that explicitly describes classes (sometimes called concepts) in a domain of discourse, designated properties (sometimes called roles/slots) of each concept describing various features and attributes of the concept, and restrictions on roles. Ontologies have a wide variety of applications in various domains, for example, semantic web, multiple-choice question generation (Vinu and Puligundla 2015;Vinu and Kumar 2017), program analysis (Pattipati, Nasre, and Puligundla 2020). Resource Description Framework Schema (RDFS) (Brickley and Guha 2004) and Web Ontology Language (OWL) (Bechhofer et al 2004) are two widely used frameworks to set up ontologies.…”