Nowadays, many developers heavily rely on feedback from bug-detection tools to help ensure the quality of the code they produce. Such tools are underlain by static analysis. It is, however, critical for analysis results to be produced fast. To this end, incremental static analysis can be used. Upon a program change, an incremental analysis updates the previous results rather than recomputing the results from scratch.Incremental static analyses may suffer from cyclic reinforcement of lattice values, where the computation of some values within the analysis relies on the values themselves, due to the abstractions made by the analysis. This can cause the incremental analysis to produce less precise results, reducing its usability.In this work, we provide a solution to cyclic reinforcement of lattice values for incremental dependency-driven analyses. We compute the information flow within the analysis and show how this information flow can be used to detect cyclic reinforcements. We establish a criterion to detect when a cyclic reinforcement contains outdated information that needs to be removed, and show how precision can be regained. Our results show that using our method, an incremental analysis produces results matching a from-scratch analysis for all but one benchmark program, at the cost of a performance hit in some cases.

Information Flow Control is important for securing applications, primarily to preserve the confidentiality and integrity of applications and the data they process.Statically determining the flows of information for security purposes helps to secure applications early in the development pipeline.However, a sound and precise static analysis is difficult to scale.Modular static analysis is a technique for improving the scalability of static analysis.In this paper, we present an approach for constructing a modular static analysis for performing Information Flow Control for higher-order, imperative programs.A modular analysis requires information about data dependencies between modules.These dependencies arise as a result of information flows between modules, and therefore we piggy-back an Information Flow Control analysis on top of an existing modular analysis.Additionally, the resulting modular Information Flow Control analysis retains the benefits of its modular character.We validate our approach by performing an Information Flow Control analysis on 9 synthetic benchmark programs that contain both explicit and implicit information flows.

Static analyses can be used by developers to compute properties of a program, enabling e.g., bug detection and program verification. However, reanalysing a program from scratch upon every change is time-consuming, especially in settings where code changes often, such as within IDEs. To avoid such full reanalyses, incremental analyses instead reuse parts of the previous analysis result, and reanalyse the changed code as necessary. While incrementality improves the analysis time, we introduce a complementary approach that further reduces the analysis time. A traditional incremental analysis updates previous analysis results without domain-specific knowledge. However, the effect of particular source code changes on analysis results can be predicted. Performing a traditional incremental analysis of the changed code might therefore be unnecessary. Instead, we propose to detect code change patterns of which the effect on analysis results can be predicted and to update these results accordingly, saving potentially expensive computations. In this paper, we explore the idea of adapting the analysis results for behaviour-preserving change patterns. In particular, we consider consistent renamings, inverted conditionals, and moved function definitions within Scheme programs. We implemented our approach and evaluated it on 30 programs. We show decreases in incremental analysis time between 3% and 99% on 25 programs that contain at least one behaviour-preserving change pattern.

A modular static analysis decomposes a program's analysis into analyses of its parts, or components. An intercomponent analysis instructs an intra-component analysis to analyse each component independently of the others. Additional analyses are scheduled for newly discovered components, and for dependent components that need to account for newly discovered component information. Modular static analyses are scalable, can be tuned to a high precision, and support the analysis of programs that are highly dynamic, featuring e.g., higher-order functions or dynamically allocated processes.In this paper, we present the engineering aspects of MAF, a static analysis framework for implementing modular analyses for higher-order languages. For any such modular analysis, the framework provides a reusable inter-component analysis and it suffices to implement its intra-component analysis. The intracomponent analysis can be composed from several interdependent and reusable Scala traits. This design facilitates changing the analysed language, as well as the analysis precision with minimal effort. We illustrate the use of MAF through its instantiation for several different analyses of Scheme programs.

One way to speed up static program analysis is to make use of today's multi-core CPUs by parallelising the analysis. Existing work on parallel analysis usually targets traditional data-How analyses for static, first-order languages such as C. Less attention has been given so far to the parallelisation of more general analyses that can also target dynamic, higherorder languages such as JavaScript. These are significantly more challenging to parallelise, as dependencies between analysis results are only discovered during the analysis itself. State-ofthe-art parallel analyses for such languages are therefore usually limited, both in their applicability and performance gains. In this work, we propose the parallelisation of modular analyses. Modular analyses compute different parts of the analysis in isolation of one another, and therefore offer inherent opportunities for parallelisation that have not been explored so far. In addition, they can be used to develop a general class of analysers for dynamic, higher-order languages. We present a parallel variant of the worklist algorithm that is used to drive such modular analyses. To further speed up its convergence, we show how this algorithm can exploit the monotonicity of the analysis. Existing modular analyses can be parallelised without additional effort by instead employing this parallel worklist algorithm. We demonstrate this for MODF, an inter-procedural modular analysis, and for MODCONC, an inter-process modular analysis. For MODCONC, we reveal an additional opportunity to exploit even more parallelism in the analysis. Our parallel worklist algorithm is implemented and integrated into MAF, a framework for modular program analysis. Using a set of Scheme benchmarks for MODF, we usually observe speedups between 3× and 8× when using 4 workers, and speedups between 8× and 32× when using 16 workers. For MODCONC, we achieve a maximum speedup of 15×.

Static analyses are used to gain more confidence in changes made by developers. To be of most use, such analyses must deliver feedback fast. Therefore, incremental static analyses update previous results rather than entirely recompute them. This reduces the analysis time upon a program change, and makes the analysis well-suited for environments where the code base is frequently updated, such as in IDEs and CI pipelines.In this work, we present a general approach to render a modular static analysis for highly dynamic programs incremental, by exploiting dependencies between intermediate analysis results. Modular analyses divide a program in interdependent parts that are analysed in isolation. The dependencies between these parts stem, for example, from the use of shared variables within the program. Our incrementalisation approach leverages the modularity of the analysis together with the dependencies that it reifies to compute and bound the impact of changes. This way, only the affected parts of the result need to be reanalysed, and unnecessary recomputations are avoided.We apply our approach to both a function-modular and a thread-modular analysis and evaluate it by comparing an incremental update of an existing result to a full reanalysis. We find reductions of the analysis time from 6% to 99% on 14 out of 16 benchmark programs, and on most programs the impact on precision is limited. On 7 of the programs, reanalysis time is reduced by more than 75%, showing that our approach results in fast incremental updates.