Assessing the Impact of Execution Environment on Observation-Based Slicing

TL;DR

This paper investigates how execution environment variations affect observation-based program slicing, proposing an approach that validates slices across multiple environments to improve accuracy and consistency.

Contribution

It introduces nVORBS, a method that validates program slices in multiple environments, addressing environment-induced differences in observation-based slicing.

Findings

Environment influences slicing results significantly.

Validation across multiple environments reduces slice discrepancies.

Potential for applying metamorphic testing to enhance dynamic analysis.

Abstract

Program slicing reduces a program to a smaller version that retains a chosen computation, referred to as a slicing criterion. One recent multi-lingual slicing approach, observation-based slicing (ORBS), speculatively deletes parts of the program and then executes the code. If the behavior of the slicing criteria is unchanged, the speculative deletion is made permanent. While this makes ORBS language agnostic, it can lead to the production of some non-intuitive slices. One particular challenge is when the execution environment plays a role. For example, ORBS will delete the line "a = 0" if the memory location assigned to a contains zero before executing the statement, since deletion will not affect the value of a and thus the slicing criterion. Consequently, slices can differ between execution environments due to factors such as initialization and call stack reuse. The technique considered, nVORBS, attempts to ameliorate this problem by validating a candidate slice in n different execution environments. We conduct an empirical study to collect initial insights into how often the execution environment leads to slice differences. Specifically, we compare and contrast the slices produced by seven different instantiations of nVORBS. Looking forward, the technique can be seen as a variation on metamorphic testing, and thus suggests how ideas from metamorphic testing might be used to improve dynamic program analysis.