Towards Safe Refactoring for Intelligent Parallelization of Java 8 Streams

Towards Safe Refactoring for Intelligent Parallelization of Java 8 Streams

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  Towards Safe Refactoringfor Intelligent Parallelization of Java 8 Streams Yiming Tang1 Raffi Khatchadourian1 Mehdi Bagherzadeh2 Syed Ahmed2 1 City University of New York (ponder@hunter.cuny.edu) 2 Oakland University Introduction The Java 8 Stream API sets forth a promising new programming model that incorporates functional-like, MapReduce-style features into a mainstream programming language. Problem Developers must manually determine whether running streams in parallel is efficient yet interference-free. Using streams correctly and efficiently requires many subtle considerations that may not be immediately evident. Manual analysis and refactoring can be error- and omission-prone. Automated Tool Our Eclipse Plug-in, based on ordering and typestate analysis, automatically identifies and executes refactoring opportunities where improvements can be made to Java 8 Stream code. The parallelization is “intelligent” as it carefully considers each context and may actually result in de-parallelization. Flowchart Contributions We devise an automated refactoring approach that assists developers in writing optimal stream code. The approach determines when it is safe and advantageous to convert streams to parallel and optimize parallel streams. A case study is performed on the applicability of the approach. Refactorings 1 Convert Sequential Stream to Parallel. Determines if it is advantageous and safe to convert a sequential stream to parallel. 2 Optimize Parallel Stream. Decides which transformations can improve the performance of a parallel stream, including unordering and converting to sequential. Code Snippet of Widget Collection Processing Using the Java 8 Steam API 1 Collection<Widget> unorderedWidgets = 2 new HashSet<>(); 3 List<Widget> sortedWidgets = 4 unorderedWidgets 5 .stream() 6 .sorted(Comparator.comparing( 7 Widget::getWeight)) 8 .collect(Collectors.toList()); 9 Collection<Widget> orderedWidgets = 10 new ArrayList<>(); 11 Set<Double> distinctWeightSet = 12 orderedWidgets 13 .stream().parallel() 14 .map(Widget::getWeight).distinct() 15 .collect(Collectors.toCollection( 16 TreeSet::new)); (a) Stream code snippet prior to refactoring. 1 Collection<Widget> unorderedWidgets = 2 new HashSet<>(); 3 List<Widget> sortedWidgets = 4 unorderedWidgets 5 .stream()parallelStream() 6 .sorted(Comparator.comparing( 7 Widget::getWeight)) 8 .collect(Collectors.toList()); 9 Collection<Widget> orderedWidgets = 10 new ArrayList<>(); 11 Set<Double> distinctWeightSet = 12 orderedWidgets 13 .stream().parallel() 14 .map(Widget::getWeight).distinct() 15 .collect(Collectors.toCollection( 16 TreeSet::new)); (b) Improved stream client code via refactoring. Typestate Analysis Our in-progress approach uses typestate analysis to determine stream attributes when a terminal operation is issued. A typestate variant is being developed since operations like sorted() return (possibly) new streams derived from the receiver with their attributes altered. Labeled transition systems (LTSs) are used for execution mode and ordering. Figure: LTS for execution mode. Figure: LTS for ordering. Preliminary Experimental Results projects candidate streams refactorable streams experiments 1 0 threeten-extra 2 2 jOOQ 3 0 dari 4 0 JacpFX 4 3 bootique 5 0 jdk8-experiments 16 4 htm.java 21 7 jetty-project 22 7 streamql 22 2 java-design-patterns 28 17 Grand Total 128 42 Table: Preliminary results summary. Figure: Refactoring precondition failures. Conclusion We have developed an automated refactoring approach that “intelligently” optimizes Java 8 stream code. Based on ordering and typestate analysis, it automatically deems when it is safe and advantageous to run stream code either sequentially or in parallel. Future Work Expand our corpus. Handle several issues between Eclipse and WALA. Formulate a transformation algorithm. Incorporate additional reductions like those involving maps. International Conference on Software Engineering, May 27–June 3, 2018, Gothenburg, Sweden