Prof. Eric Bodden, Ph.D. » Analyzing Software Product Lines in Minutes instead of Years (updated)

Analyzing Software Product Lines in Minutes instead of Years (updated)

Eric | February 18, 2013


In our new publication SPLLIFT — Statically Analyzing Software Product Lines in Minutes Instead of Years (to appear at PLDI’13) we show how to efficiently conduct inter-procedural, flow-sensitive, context-sensitive data-flow analysis for software product lines. Previously, such analyses would have taken years, due to the many software configurations a product line encodes. Our approach SPLlift processes the entire product line at once, and typically within minutes, without any loss of precision. It works for any IFDS-based data-flow analysis. SPLlift is available as an open-source extension to our IFDS/IDE solver Heros. To access our benchmark data, click here. This is joint work with Mira Mezini, Claus Brabrand, Társis Tolêdo, Márcio Ribeiro and Paulo Borba.Our full abstract:

A software product line (SPL) encodes a potentially large variety of software products as variants of some common code base. Up until now, re-using traditional static analyses for SPLs was virtually intractable, as it required programmers to generate and analyze all products individually. In this work, however, we show how an important class of existing inter-procedural static analyses can be transparently lifted to SPLs. Without requiring programmers to change a single line of code, our approach SPLLIFT automatically converts any analysis formulated for traditional programs within the popular IFDS framework for inter-procedural, finite, distributive, subset problems to an SPL-aware analysis formulated in the IDE framework, a well-known extension to IFDS. Using a full implementation based on Heros, Soot, CIDE and JavaBDD, we show that with SPLLIFT one can reuse IFDS-based analyses without changing a single line of code. Through experiments using three static analyses applied to four Java-based product lines, we were able to show that our approach produces correct results and outperforms the traditional approach by several orders of magnitude.