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Annotative Software Product Line Analysis Using Variability-Aware Datalog
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DOI：10.1109/TSE.2022.3175752.png)
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摘要
En 中文
Applying program analyses to Software Product Lines (SPLs) has been a fundamental research problem at the intersection of Product Line Engineering and software analysis. Different attempts have been made to lift particular product-level analyses to run on the entire product line. In this paper, we tackle the class of Datalog-based analyses (e.g., pointer and taint analyses), study the theoretical aspects of lifting Datalog inference, and implement a lifted inference algorithm inside the Souffle Datalog engine. We evaluate our implementation on a set of Java and C-language benchmark annotative software product lines. We show significant savings in processing time and fact database size (billions of times faster on one of the benchmarks) compared to brute-force analysis of each product individually.
Keyword:
Inference algorithms
Engines
Codes
Software product lines
Software
Databases
Color
datalog
program analysis
pointer analysis
lifting
variability
doop
Souffle