Alias analysis for optimization of dynamic languages

Abstract

Dynamic languages such as Python allow programs to be written more easily using high-level constructs such as comprehensions for queries and using generic code. Efficient execution of programs then requires powerful optimizationsincrementalization of expensive queries and specialization of generic code. Effective incrementalization and specialization of dynamic languages require precise and scalable alias analysis.This paper describes the development and experimental evaluation of a may-alias analysis for a full dynamic objectoriented language, for program optimization by incrementalization and specialization. The analysis is flow-sensitive; we show that this is necessary for effective optimization of dynamic languages. It uses precise type analysis and a powerful form of context sensitivity, called trace sensitivity, to further improve analysis precision. It uses a compressed representation to significantly reduce the memory used by flowsensitive analyses. We evaluate the effectiveness of this analysis and 17 variants of it for incrementalization and specialization of Python programs, and we evaluate the precision, memory usage, and running time of these analyses on programs of diverse sizes. The results show that our analysis has acceptable precision and efficiency and represents the best trade-off between them compared to the variants.

From Clarity to Efficiency for Distributed Algorithms

“…Once these are established, updates can be determined using previously studied analysis methods, for example, [33] and [53]. Incremental computation.…”

“…It optionally interfaces with an incrementalizer to apply incrementalization before generating code. Applying incrementalization uses the methods and implementation from previous work: a library of incrementalization rules was developed, manually but mostly following a systematic method [53,57], and applied automatically using InvTS [33,50]. A set of heuristics is currently used to select the best program generated from incrementalizing differently converted aggregations.…”

Python predictive analysis for bug detection

“…As such, we need to address some new challenges such as encoding a variable in various types, handling unexecuted paths, and modeling attribute set changes. Type inference for Python [23,24,25] has also been proposed. The work is mainly based on abstract interpretation, which may have difficulty handling dynamic features.…”

“…Existing analysis for Python and other dynamic languages can be classified to three kinds. Static analysis [25,23,2,3] (e.g., abstract interpretation and type inference) analyze program statically. They have limited effectiveness due to the dynamic features in Python.…”

Static Type Analysis for Python

“…With PType, we could detect type errors of web applications or some other Python programs. In addition, the work of Michael Gorbovitski [1] shows that with type information some alias pairs could be eliminated if type mismatch found, thus would increase the precision and efficiency of point-to analysis. 1 …”