Probabilistic Slicing for Predictive Impact Analysis

Program slicing is a technique that determines which statements in a program affect or are affected by another statement in that program. Static forward slicing, in particular, can be used for impact analysis by identifying all potential effects of changes in software. This information helps developers design and test their changes. Unfortunately, static slicing is too imprecise—it often produces large sets of potentially affected statements, limiting its usefulness. To reduce the resulting set of statements, other forms of slicing have been proposed, such as dynamic slicing and thin slicing, but they can miss relevant statements. In this paper, we present a new technique, called Probabilistic Slicing (p-slicing), that augments a static forward slice with a relevance score for each statement by exploiting the observation that not all statements have the same probability of being affected by a change. P-slicing can be used, for example, to focus the attention of developers on the “most impacted” parts of the program first. It can also help testers, for example, by estimating the difficulty of “killing” a particular mutant in mutation testing and prioritizing test cases. We also present an empirical study that shows the effectiveness of p-slicing for predictive impact analysis and we discuss potential benefits for other tasks.