Modular Static Program Analysis

Cousot, Patrick; Cousot, Radhia

doi:10.1007/3-540-45937-5_13

Cited by 120 publications

(122 citation statements)

References 61 publications

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“…To this end, predictive FDD builds upon static code analysis [1,13], but augments it with knowledge about runtime behavior. This allows us to generate powerful warnings and predictions, which would not be possible based on static analysis alone.…”

Section: Paper Contribution and Outlinementioning

confidence: 99%

Runtime metric meets developer - Building better cloud applications using feedback

Cito

Leitner

Gall

et al. 2015

Preprint

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A unifying theme of many ongoing trends in software engineering is a blurring of the boundaries between building and operating software products. In this paper, we explore what we consider to be the logical next step in this succession: integrating runtime monitoring data from production deployments of the software into the tools developers utilize in their daily workflows (i.e., IDEs) to enable tighter feedback loops. We refer to this notion as feedback-driven development (FDD).This more abstract FDD concept can be instantiated in various ways, ranging from IDE plugins that implement feedback-driven refactoring and code optimization to plugins that predict performance and cost implications of code changes prior to even deploying the new version of the software. We demonstrate existing proof-of-concept realizations of these ideas and illustrate our vision of the future of FDD and cloud-based software development in general. Further, we discuss the major challenges that need to be solved before FDD can achieve mainstream adoption.

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Section: Paper Contribution and Outlinementioning

confidence: 99%

Runtime metric meets developer - Building better cloud applications using feedback

Cito

Leitner

Gall

et al. 2015

Preprint

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“…The idea of delaying parts of the analysis has been explored before in interprocedural analyses to improve context-sensitivity [10,32] or to speed up bottom-up whole-program analyses [14]. Our work shows that this approach also brings benefits to the analysis of programs with callbacks, and is in fact critical to its applicability.…”

Section: Related Workmentioning

confidence: 87%

Effect Analysis for Programs with Callbacks

Kneuss

Kunčak

Suter

2014

Verified Software: Theories, Tools, Experiments

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We introduce a precise interprocedural effect analysis for programs with mutable state, dynamic object allocation, and dynamic dispatch. Our analysis is precise even in the presence of dynamic dispatch where the context-insensitive estimate on the number of targets is very large. This feature makes our analysis appropriate for programs that manipulate first-class functions (callbacks). We first present a framework in which programs are enriched with special effect statements, and define the semantics of both program and effect statements as relations on states. Our framework defines a program composition operator that is sound with respect to relation composition. Computing the summary of a procedure then consists of composing all its program statements to produce a single effect statement. We propose a strategy for applying the composition operator in a way that balances precision and efficiency.We instantiate this framework with a domain for tracking read and write effects, where relations on program states are abstracted as graphs. We implemented the analysis as a plugin for the Scala compiler. We analyzed the Scala standard library containing 58K methods and classified them into several categories according to their effects. Our analysis proves that over one half of all methods are pure. We also analyze how context sensitivity and composition operator application strategies impact the analysis precision and performance.

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“…Another related technique is that of modular analysis [12,25]. There, the main aim is to improve the scalability of analysis by reducing the memory consumption, which is a common bottle-neck of global analysis.…”

Section: Related Workmentioning

confidence: 99%

A multi-domain incremental analysis engine and its application to incremental resource analysis

Albert

Fernández

Puebla

et al. 2015

Theoretical Computer Science

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The aim of incremental analysis is, given a program, its analysis results, and a series of changes to the program, to obtain the new analysis results as efficiently as possible and, ideally, without having to (re-)analyze fragments of code which are not affected by the changes. Incremental analysis can significantly reduce both the time and the memory requirements of analysis. The first contribution of this article is a multi-domain incremental fixed-point algorithm for a sequential Java-like language. The algorithm is multi-domain in the sense that it interleaves the (re-)analysis for multiple domains by taking into account dependencies among them. Importantly, this allows the incremental analyzer to invalidate only those analysis results previously inferred by certain dependent domains. The second contribution is an incremental resource usage analysis which, in its first phase, uses the multi-domain incremental fixed-point algorithm to carry out all global pre-analyses required to infer cost in an interleaved way. Such resource analysis is parametric on the cost metrics one wants to measure (e.g., number of executed instructions, number of objects created, etc.). Besides, we present a novel form of cost summaries which allows us to incrementally reconstruct only those components of cost functions affected by the changes. Experimental results in the costa system show that the proposed incremental analysis provides significant performance gains, ranging from a speedup of 1.48 up to 5.13 times faster than non-incremental analysis.

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Modular Static Program Analysis

Cited by 120 publications

References 61 publications

Runtime metric meets developer - Building better cloud applications using feedback

Runtime metric meets developer - Building better cloud applications using feedback

Effect Analysis for Programs with Callbacks

A multi-domain incremental analysis engine and its application to incremental resource analysis

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