Rethinking Soot for summary-based whole-program analysis

Yan, Dacong; Xu, Guoqing; Rountev, Atanas

doi:10.1145/2259051.2259053

Cited by 15 publications

(14 citation statements)

References 41 publications

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“…Our work is also similar to the works that accelerate program analysis [24], [25], [26], [27]. Kulkarni et al [24] proposed a technique to accelerate program analysis in Datalog.…”

Section: Related Worksupporting

confidence: 67%

On Accelerating Source Code Analysis at Massive Scale

Upadhyaya

Rajan

2018

IIEEE Trans. Software Eng.

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Encouraged by the success of data-driven software engineering (SE) techniques that have found numerous applications e.g. in defect prediction, specification inference, etc, the demand for mining and analyzing source code repositories at scale has significantly increased. However, analyzing source code at scale remains expensive to the extent that data-driven solutions to certain SE problems are beyond our reach today. Extant techniques have focussed on leveraging distributed computing to solve this problem, but with a concomitant increase in computational resource needs. This work proposes a technique that reduces the amount of computation performed by the ultra-large-scale source code mining task. Our key idea is to analyze the mining task to identify and remove the irrelevant portions of the source code, prior to running the mining task. We show a realization of our insight for mining and analyzing massive collections of control flow graphs of source codes. Our evaluation using 16 classical control-/data-flow analyses that are typical components of mining tasks and 7 Million CFGs shows that our technique can achieve on average 40% reduction in the task computation time. Our case studies demonstrates the applicability of our technique to massive scale source code mining tasks.

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“…Our work is also similar to the works that accelerate program analysis [24], [25], [26], [27]. Kulkarni et al [24] proposed a technique to accelerate program analysis in Datalog.…”

Section: Related Worksupporting

confidence: 67%

On Accelerating Source Code Analysis at Massive Scale

Upadhyaya

Rajan

2018

IIEEE Trans. Software Eng.

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“…Summarizing libraries. Rountev et al [22,31] propose an approach to summarize library code independent of any client code. They address the general class of interprocedural distributive environment (IDE) dataflow problems but use a graph representation of dataflow summary functions that need to be instantiated at every call site.…”

Section: Related Workmentioning

confidence: 99%

Accelerating program analyses by cross-program training

et al. 2016

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Practical programs share large modules of code. However, many program analyses are ineffective at reusing analysis results for shared code across programs. We present POLYMER, an analysis optimizer to address this problem. POLYMER runs the analysis offline on a corpus of training programs and learns analysis facts over shared code. It prunes the learnt facts to eliminate intermediate computations and then reuses these pruned facts to accelerate the analysis of other programs that share code with the training corpus.We have implemented POLYMER to accelerate analyses specified in Datalog, and apply it to optimize two analyses for Java programs: a call-graph analysis that is flow-and context-insensitive, and a points-to analysis that is flow-and context-sensitive. We evaluate the resulting analyses on ten programs from the DaCapo suite that share the JDK library. POLYMER achieves average speedups of 2.6× for the callgraph analysis and 5.2× for the points-to analysis.

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“…Some approaches in this category ⋆ Partially supported by a TCS Fellowship are: call strings method [21], its value-based variants [10,17] and the tabulation based functional method [18,21]. By contrast, bottom-up approaches avoid analyzing callees multiple times by constructing summary flow functions which are used at call sites to incorporate the effect of procedure calls [3,6,13,19,21,[23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Flow- and Context-Sensitive Points-To Analysis Using Generalized Points-To Graphs

2016

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Computing precise (fully flow-sensitive and context-sensitive) and exhaustive (as against demand driven) points-to information is known to be computationally expensive. Therefore many practical tools approximate the points-to information trading precision for efficiency. This often has adverse impact on computationally intensive analyses such as model checking. Past explorations in top-down approaches of fully flow-and context-sensitive points-to analysis (FCPA) have not scaled. We explore the alternative of bottom-up interprocedural approach which constructs summary flow functions for procedures to represent the effect of their calls. This approach has been effectively used for many analyses. However, this approach seems computationally expensive for FCPA which requires modelling unknown locations accessed indirectly through pointers. Such accesses are commonly handled by using placeholders to explicate unknown locations or by using multiple call-specific summary flow functions.We generalize the concept of points-to relations by using the counts of indirection levels leaving the unknown locations implicit. This allows us to create summary flow functions in the form of generalized points-to graphs (GPGs) without the need of placeholders. By design, GPGs represent both memory (in terms of classical points-to facts) and memory transformers (in terms of generalized points-to facts). We perform FCPA by progressively reducing generalized points-to facts to classical points-to facts. GPGs distinguish between may and must pointer updates thereby facilitating strong updates within calling contexts.The size of GPG for a procedure is linearly bounded by the number of variables and is independent of the number of statements in the procedure. Empirical measurements on SPEC benchmarks show that GPGs are indeed compact in spite of large procedure sizes. This allows us to scale FCPA to 158 kLoC using GPGs (compared to 35 kLoC reported by liveness-based FCPA). At a practical level, GPGs hold a promise of efficiency and scalability for FCPA without compromising precision. At a more general level, GPGs provide a convenient abstraction of memory in presence of pointers. Static analyses that are influenced by pointers may be able to use GPGs by combining them with their original abstractions.

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Rethinking Soot for summary-based whole-program analysis

Cited by 15 publications

References 41 publications

On Accelerating Source Code Analysis at Massive Scale

On Accelerating Source Code Analysis at Massive Scale

Accelerating program analyses by cross-program training

Flow- and Context-Sensitive Points-To Analysis Using Generalized Points-To Graphs

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