Learning to Optimize the Alloy Analyzer

Wang, Wenxi; Wang, Kaiyuan; Zhang, Mengshi; Khurshid, Sarfraz

doi:10.1109/icst.2019.00031

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…Wang et al [45] correlate Alloy model features with analysis time. They examine a number of static features of Alloy models at three different levels: an Alloy model, its Kodkod model, and its propositional logic (SAT) model.…”

Section: Related Workmentioning

confidence: 99%

Static Profiling of Alloy Models

Eid

Day

2023

IIEEE Trans. Software Eng.

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Modeling of software-intensive systems using formal declarative modeling languages offers a means of managing software complexity through the use of abstraction and early identification of correctness issues by formal analysis. Alloy is one such language used for modeling systems early in the development process. Little work has been done to study the styles and techniques commonly used in Alloy models. We present the first static analysis study of Alloy models. We investigate research questions that examine a large corpus of 1,652 Alloy models. To evaluate these research questions, we create a methodology that leverages the power of ANTLR pattern matching and the query language XPath. Our research questions are split into two categories depending on their purpose. The Model Characteristics category aims to identify what language constructs are used commonly. Modeling Practices questions are considerably more complex and identify how modelers are using Alloy's constructs. We also evaluate our research questions on a subset of models from our corpus written by expert modelers. We compare the results of the expert corpus to the results obtained from the general corpus to gain insight into how expert modelers use the Alloy language. We draw conclusions from the findings of our research questions and present actionable items for educators, language and environment designers, and tool developers. Actionable items for educators are intended to highlight underutilized language constructs and features, and help student modelers avoid discouraged practices. Actionable items aimed at language designers present ways to improve the Alloy language by adding constructs or removing unused ones based on trends identified in our corpus of models. The actionable items aimed at environment designers address features to facilitate model creation. Actionable items for tool developers provide suggestions for back-end optimizations.

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Section: Related Workmentioning

confidence: 99%

Static Profiling of Alloy Models

Eid

Day

2023

IIEEE Trans. Software Eng.

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“…Third, our experiments use a single SAT solver in the default configuration as provided by Alloy. It is well-known that different SAT solver may lead to different performance results (Wang et al 2019). To mitigate this threat of varying performance results, we have selected SAT4J for all experiments as the default and reportedly most stable SAT solver bundled with Alloy.…”

Section: Threats To Validitymentioning

confidence: 99%

On Implementing Open World Semantic Differencing for Class Diagrams.

Ringert,

Rumpe,

Stachon

2023

JOT

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Semantic difference analyses support engineers in understanding model-changes on the semantic level and thereby assist change management in Model Driven Development (MDD). A semantic differencing operator for Class Diagrams (CDs) compares two input models based on their legal instances and outputs object structures that are permitted by the first but not the second input-CD. Under the closed-world assumption only instances of explicitly-modeled elements are permitted. This closed-world approach may be less suited for semantic refinement checking in early design phases, where new elements are added to a CD in order to refine it. Instead, an open-world approach can be utilized. In this paper, we outline, evaluate and compare two approaches for extending CDDiff, an existing closed-world semantic differencing operator for CDs, to an open-world operator: The first approach reduces the open-world problem to a bounded search via Alloy. The second approach transforms the input-CDs and reduces the open-world problem to closed-world semantic differencing. It also allows for combining open-and closed-world interpretations of model-elements.

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“…A lot of work has been done to improve [20,22,24] and extend [10][11][12][13]16,19,25,[28][29][30][31]33] Alloy. We discuss work that is closely related to iAlloy.…”

Section: Related Workmentioning

confidence: 99%

Incremental Analysis of Evolving Alloy Models

Wang

Gligoric

et al. 2019

Tools and Algorithms for the Construction and Analysis of Systems

Self Cite

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Alloy is a well-known tool-set for building and analyzing software designs and models. Alloy's key strengths are its intuitive notation based on relational logic, and its powerful analysis engine backed by propositional satisfiability (SAT) solvers to help users find subtle design flaws. However, scaling the analysis to the designs of real-world systems remains an important technical challenge. This paper introduces a new approach, iAlloy, for more efficient analysis of Alloy models. Our key insight is that users often make small and frequent changes and repeatedly run the analyzer when developing Alloy models, and the development cost can be reduced with the incremental analysis over these changes. iAlloy is based on two techniques-a static technique based on a lightweight impact analysis and a dynamic technique based on solution re-use-which in many cases helps avoid potential costly SAT solving. Experimental results show that iAlloy significantly outperforms Alloy analyzer in the analysis of evolving Alloy models with more than 50% reduction in SAT solver calls on average, and up to 7x speedup.

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Learning to Optimize the Alloy Analyzer

Cited by 8 publications

References 36 publications

Static Profiling of Alloy Models

Static Profiling of Alloy Models

On Implementing Open World Semantic Differencing for Class Diagrams.

Incremental Analysis of Evolving Alloy Models

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