Prioritizing Change-Impact Analysis via Semantic Program-Dependence Quantification

doi:10.1109/TR.2015.2481000

Prioritizing Change-Impact Analysis via Semantic Program-Dependence Quantification

2016-09-01

8

PRE

AI

DOI：10.1109/TR.2015.2481000

Original

Original request for help

Save

Abstract

En 中文

Software is constantly changing. To ensure the quality of this process, when preparing to change a program, developers must first identify the main consequences and risks of modifying the program locations they intend to change. This activity is called change-impact analysis. However, existing impact analysis suffers from two major problems: coarse granularity and large size of the resulting impact sets. Finer-grained analyses such as slicing give more detailed impact sets which, however, are also even larger in size. While various impact-set reduction approaches have been proposed at different levels of granularity, the challenge persists as very-large impact sets are still produced, impeding the adoption of impact analysis due to the great costs of inspecting those impact sets. To address these challenges, we present a novel dynamic-analysis technique called SENSA which combines sensitivity analysis and execution differencing. SENSA not only provides fine-grained (statement-level) impact sets but also prioritizes potential impacts via semantic-dependence quantification for program slices. We evaluated the benefits of impact prioritization using SENSA with respect to static and dynamic forward slicing via an extensive empirical study of open-source Java applications and three case studies. Our results show that SENSA can offer much better cost-effectiveness than slicing in assisting developers with impact inspection and fault cause-effect understanding.

Keywords:

Dependence quantification

execution differencing

impact prediction

impact prioritization

sensitivity analysis

Prioritizing Change-Impact Analysis via Semantic Program-Dependence Quantification

Abstract

AI Summary

Journal

Organization