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Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs

Authors Madhurima Chakraborty, Renzo Olivares, Manu Sridharan, Behnaz Hassanshahi

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Author Details

Madhurima Chakraborty
  • University of California, Riverside, CA, USA
Renzo Olivares
  • University of California, Riverside, CA, USA
Manu Sridharan
  • University of California, Riverside, CA, USA
Behnaz Hassanshahi
  • Oracle Labs, Brisbane, Australia

Funding

This research was supported in part by a gift from Oracle Labs and by the National Science Foundation under grant CCF-2007024. This research was partially sponsored by the OUSD(R&E)/RT&L and was accomplished under Cooperative Agreement Number W911NF-20-2-0267. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARL and OUSD(R&E)/RT&L or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

Cite As Get BibTex

Madhurima Chakraborty, Renzo Olivares, Manu Sridharan, and Behnaz Hassanshahi. Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs. In 36th European Conference on Object-Oriented Programming (ECOOP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 222, pp. 3:1-3:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.ECOOP.2022.3

Abstract

Building sound and precise static call graphs for real-world JavaScript applications poses an enormous challenge, due to many hard-to-analyze language features. Further, the relative importance of these features may vary depending on the call graph algorithm being used and the class of applications being analyzed. In this paper, we present a technique to automatically quantify the relative importance of different root causes of call graph unsoundness for a set of target applications. The technique works by identifying the dynamic function data flows relevant to each call edge missed by the static analysis, correctly handling cases with multiple root causes and inter-dependent calls. We apply our approach to perform a detailed study of the recall of a state-of-the-art call graph construction technique on a set of framework-based web applications. The study yielded a number of useful insights. We found that while dynamic property accesses were the most common root cause of missed edges across the benchmarks, other root causes varied in importance depending on the benchmark, potentially useful information for an analysis designer. Further, with our approach, we could quickly identify and fix a recall issue in the call graph builder we studied, and also quickly assess whether a recent analysis technique for Node.js-based applications would be helpful for browser-based code. All of our code and data is publicly available, and many components of our technique can be re-used to facilitate future studies.

Subject Classification

ACM Subject Classification
  • Theory of computation → Program analysis
Keywords
  • JavaScript
  • call graph construction
  • static program analysis

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