Systematic derivation of correct variability-aware program analyses

Midtgaard, Jan; Dimovski, Aleksandar S.; Brabrand, Claus; Wąsowski, Andrzej

doi:10.1016/j.scico.2015.04.005

Cited by 47 publications

(33 citation statements)

References 37 publications

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“…We also plan to consider liveness properties (such as termination) and expectation queries [30]. An interesting direction for future work would also be to consider general probabilistic programs [19], as well as program families implemented with #ifdef-s from the C-preprocessor where we can use lifted static analyses to efficiently analyze all variants of the family simultaneously at once [14,24,15,16].…”

Section: Resultsmentioning

confidence: 99%

Computing Program Reliability Using Forward-Backward Precondition Analysis and Model Counting

Dimovski¹,

Legay

2020

Fundamental Approaches to Software Engineering

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The goal of probabilistic static analysis is to quantify the probability that a given program satisfies/violates a required property (assertion). In this work, we use a static analysis by abstract interpretation and model counting to construct probabilistic analysis of deterministic programs with uncertain input data, which can be used for estimating the probabilities of assertions (program reliability). In particular, we automatically infer necessary preconditions in order a given assertion to be satisfied/violated at run-time using a combination of forward and backward static analyses. The focus is on numeric properties of variables and numeric abstract domains, such as polyhedra. The obtained preconditions in the form of linear constraints are then analyzed to quantify how likely is an input to satisfy them. Model counting techniques are employed to count the number of solutions that satisfy given linear constraints. These counts are then used to assess the probability that the target assertion is satisfied/violated. We also present how to extend our approach to analyze non-deterministic programs by inferring sufficient preconditions. We built a prototype implementation and evaluate it on several interesting examples.

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Section: Resultsmentioning

confidence: 99%

Computing Program Reliability Using Forward-Backward Precondition Analysis and Model Counting

Dimovski¹,

Legay

2020

Fundamental Approaches to Software Engineering

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“…Syntactic transformation techniques have been suggested for lifting abstract interpretation analyses to SPLs [22]. This line of work outlines a systematic approach to lifting abstract interpretation analyses, together with correctness proofs.…”

Section: Related Workmentioning

confidence: 99%

“…How to leverage this commonality and analyze the whole product line at once, bringing the total analysis time down, is a fundamental research problem at the intersection of Product Line Engineering and software analysis. Different attempts have been made to lift individual analyses to run on product lines [4,9,11,16,18,22,24]. Those attempts show significant time savings when the SPL is analyzed as a whole compared to brute-force analysis.…”

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confidence: 99%

Lifting Datalog-based analyses to software product lines

Shahin

Chećhik

Salay

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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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 Soufflé 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. CCS CONCEPTS• Software and its engineering → Automated static analysis; Software design techniques.

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“…The simplest brute-force approach that uses a preprocessor to generate all variants of a family, and then applies an existing off-the-shelf single-program analyzer to each individual variant, one-by-one, is very inefficient [3,27]. Therefore, we use so-called lifted (family-based) static analyses [3,22,27], which analyze all variants of the family simultaneously without generating any of the variants explicitly. They take as input the common code base, which encodes all variants of a program family, and produce precise analysis results corresponding to all variants.…”

Section: Introductionmentioning

confidence: 99%

A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features

Dimovski¹,

Apel

Legay

2021

Fundamental Approaches to Software Engineering

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Lifted (family-based) static analysis by abstract interpretation is capable of analyzing all variants of a program family simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted analysis domain are tuples, which maintain one property per variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces.The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and $$\texttt {\#if}$$ # if directives). In this work, we introduce a new symbolic representation of the lifted abstract domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. The elements of the new lifted domain are constraint-based decision trees, where decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. To illustrate the potential of this representation, we have implemented an experimental lifted static analyzer, called SPLNum$$^2$$ 2 Analyzer, for inferring invariants of C programs. An empirical evaluation on BusyBox and on benchmarks from SV-COMP yields promising preliminary results indicating that our decision trees-based approach is effective and outperforms the baseline tuple-based approach.

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Systematic derivation of correct variability-aware program analyses

Cited by 47 publications

References 37 publications

Computing Program Reliability Using Forward-Backward Precondition Analysis and Model Counting

Computing Program Reliability Using Forward-Backward Precondition Analysis and Model Counting

Lifting Datalog-based analyses to software product lines

A Decision Tree Lifted Domain for Analyzing Program Families with Numerical Features

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