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Lifting Datalog-Based Analyses to Software Product Lines

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Ramy Shahin, Marsha Chechik, Rick Salay

Software Engineering · Submitted 4 Jul 2019

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · asserted 1.00 read the record →

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arXiv ID1907.02192 (version 2)
Pith IDOMNXTJS2, Pith's permanent record of this paper and its review
CitationMarsha Chechik, Ramy Shahin, Rick Salay. "Lifting Datalog-Based Analyses to Software Product Lines." arXiv:1907.02192 (2019). https://pith.science/paper/OMNXTJS2
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,252 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.07 · human participants 0.02 · controlled comparison 0.84 · randomized assignment 0.01 · states quantitative result 0.89 · compares with prior results 0.89 · mentions code or data availability 0.05.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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 Souffl\'e Datalog engine. We evaluate our implementation on a set of benchmark 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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Applying program analyses to Software Product Lines (SPLs) has been a fundamental research problem at the intersection of Product Line Engineering and software analysis. background 1.00 0.04 0.02 asserted 1.00
s1 Different attempts have been made to "lift" particular product-level analyses to run on the entire product line. background 1.00 0.04 0.02 asserted 0.99
s2 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 Souffl\'e Datalog engine. contribution 1.00 0.96 0.07 asserted 0.97
s3 We evaluate our implementation on a set of benchmark product lines. method 0.87 0.93 0.03 asserted 0.83
s4 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. result 1.00 0.96 0.81 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.89.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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 Souffl\'e Datalog engine. We evaluate our implementation on a set of benchmark 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.

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