�ݺ�ߣshows by User: LisaHua

�ݺ�ߣshows by User: LisaHua / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: LisaHua / Fri, 14 Jul 2017 22:40:32 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: LisaHua EdSketch: Execution-Driven Sketching for Java /slideshow/edsketch-executiondriven-sketching-for-java/77890946 edsketchspin17-170714224032
Sketching is a relatively recent approach to program synthesis, which has shown much promise. The key idea in sketching is to allow users to write partial programs that have “holes” and provide test harnesses or reference implementations, and let synthesis tools create program fragments that the holes such that the resulting complete program has the desired functionality. Traditional solutions to the sketching problem perform a translation to SAT and employ CEGIS. While e ective for a range of programs, when applied to real applications, such translation-based approaches have a key limitation: they require either translating all relevant libraries that are invoked directly or indirectly by the given sketch – which can lead to impractical SAT problems – or creating models of those libraries – which can require much manual effort. is paper introduces execution-driven sketching, a novel approach for synthesis of Java programs using a backtracking search that is commonly employed in so ware model checkers. e key novelty of our work is to introduce effective pruning strategies to effciently explore the actual program behaviors in presence of libraries and to provide a practical solution to sketching small parts of real-world applications, which may use complex constructs of modern languages, such as reflection or native calls. Our tool EdSketch embodies our approach in two forms: a stateful search based on the Java PathFinder model checker; and a stateless search based on re-execution inspired by the VeriSoft model checker. Experimental results show that EdSketch’s performance compares well with the well-known SAT-based Sketch system for a range of small but complex programs, and moreover, that EdSketch can complete some sketches that require handling complex constructs.]]>
Sketching is a relatively recent approach to program synthesis, which has shown much promise. The key idea in sketching is to allow users to write partial programs that have “holes” and provide test harnesses or reference implementations, and let synthesis tools create program fragments that the holes such that the resulting complete program has the desired functionality. Traditional solutions to the sketching problem perform a translation to SAT and employ CEGIS. While e ective for a range of programs, when applied to real applications, such translation-based approaches have a key limitation: they require either translating all relevant libraries that are invoked directly or indirectly by the given sketch – which can lead to impractical SAT problems – or creating models of those libraries – which can require much manual effort. is paper introduces execution-driven sketching, a novel approach for synthesis of Java programs using a backtracking search that is commonly employed in so ware model checkers. e key novelty of our work is to introduce effective pruning strategies to effciently explore the actual program behaviors in presence of libraries and to provide a practical solution to sketching small parts of real-world applications, which may use complex constructs of modern languages, such as reflection or native calls. Our tool EdSketch embodies our approach in two forms: a stateful search based on the Java PathFinder model checker; and a stateless search based on re-execution inspired by the VeriSoft model checker. Experimental results show that EdSketch’s performance compares well with the well-known SAT-based Sketch system for a range of small but complex programs, and moreover, that EdSketch can complete some sketches that require handling complex constructs.]]> Fri, 14 Jul 2017 22:40:32 GMT /slideshow/edsketch-executiondriven-sketching-for-java/77890946 LisaHua@slideshare.net(LisaHua) EdSketch: Execution-Driven Sketching for Java LisaHua Sketching is a relatively recent approach to program synthesis, which has shown much promise. The key idea in sketching is to allow users to write partial programs that have “holes” and provide test harnesses or reference implementations, and let synthesis tools create program fragments that the holes such that the resulting complete program has the desired functionality. Traditional solutions to the sketching problem perform a translation to SAT and employ CEGIS. While e ective for a range of programs, when applied to real applications, such translation-based approaches have a key limitation: they require either translating all relevant libraries that are invoked directly or indirectly by the given sketch – which can lead to impractical SAT problems – or creating models of those libraries – which can require much manual effort. is paper introduces execution-driven sketching, a novel approach for synthesis of Java programs using a backtracking search that is commonly employed in so ware model checkers. e key novelty of our work is to introduce effective pruning strategies to effciently explore the actual program behaviors in presence of libraries and to provide a practical solution to sketching small parts of real-world applications, which may use complex constructs of modern languages, such as reflection or native calls. Our tool EdSketch embodies our approach in two forms: a stateful search based on the Java PathFinder model checker; and a stateless search based on re-execution inspired by the VeriSoft model checker. Experimental results show that EdSketch’s performance compares well with the well-known SAT-based Sketch system for a range of small but complex programs, and moreover, that EdSketch can complete some sketches that require handling complex constructs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/edsketchspin17-170714224032-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Sketching is a relatively recent approach to program synthesis, which has shown much promise. The key idea in sketching is to allow users to write partial programs that have “holes” and provide test harnesses or reference implementations, and let synthesis tools create program fragments that the holes such that the resulting complete program has the desired functionality. Traditional solutions to the sketching problem perform a translation to SAT and employ CEGIS. While e ective for a range of programs, when applied to real applications, such translation-based approaches have a key limitation: they require either translating all relevant libraries that are invoked directly or indirectly by the given sketch – which can lead to impractical SAT problems – or creating models of those libraries – which can require much manual effort. is paper introduces execution-driven sketching, a novel approach for synthesis of Java programs using a backtracking search that is commonly employed in so ware model checkers. e key novelty of our work is to introduce effective pruning strategies to effciently explore the actual program behaviors in presence of libraries and to provide a practical solution to sketching small parts of real-world applications, which may use complex constructs of modern languages, such as reflection or native calls. Our tool EdSketch embodies our approach in two forms: a stateful search based on the Java PathFinder model checker; and a stateless search based on re-execution inspired by the VeriSoft model checker. Experimental results show that EdSketch’s performance compares well with the well-known SAT-based Sketch system for a range of small but complex programs, and moreover, that EdSketch can complete some sketches that require handling complex constructs.

EdSketch: Execution-Driven Sketching for Java from Lisa Hua

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0 Spark overview /LisaHua/spark-overview-37479609 sparkoverview-140729190732-phpapp01
This presentation gives a brief introduction of the Spark Framework and how it can be used in machine learning platform.]]>
This presentation gives a brief introduction of the Spark Framework and how it can be used in machine learning platform.]]> Tue, 29 Jul 2014 19:07:31 GMT /LisaHua/spark-overview-37479609 LisaHua@slideshare.net(LisaHua) Spark overview LisaHua This presentation gives a brief introduction of the Spark Framework and how it can be used in machine learning platform. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sparkoverview-140729190732-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This presentation gives a brief introduction of the Spark Framework and how it can be used in machine learning platform.

Spark overview from Lisa Hua

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0 Shifu plugin-trainer and pmml-adapter /slideshow/shifu-plugintrainer-andpmmladapter/37479527 5xufqw0usfilfr6rlbeb-signature-0ce7629d67ee9301560393cf10c0eb5e8300a0007fca4787601a03a2a645340a-poli-140729190307-phpapp02
Shifu (www.shifu.ml) is a fast and scalable machine learning platform. This presentation briefly describes how to convert the Logistic Regression and Neural Network model in Encog, Mahout, and Spark.]]>
Shifu (www.shifu.ml) is a fast and scalable machine learning platform. This presentation briefly describes how to convert the Logistic Regression and Neural Network model in Encog, Mahout, and Spark.]]> Tue, 29 Jul 2014 19:03:07 GMT /slideshow/shifu-plugintrainer-andpmmladapter/37479527 LisaHua@slideshare.net(LisaHua) Shifu plugin-trainer and pmml-adapter LisaHua Shifu (www.shifu.ml) is a fast and scalable machine learning platform. This presentation briefly describes how to convert the Logistic Regression and Neural Network model in Encog, Mahout, and Spark. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5xufqw0usfilfr6rlbeb-signature-0ce7629d67ee9301560393cf10c0eb5e8300a0007fca4787601a03a2a645340a-poli-140729190307-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Shifu (www.shifu.ml) is a fast and scalable machine learning platform. This presentation briefly describes how to convert the Logistic Regression and Neural Network model in Encog, Mahout, and Spark.

Shifu plugin-trainer and pmml-adapter from Lisa Hua

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0 https://cdn.slidesharecdn.com/profile-photo-LisaHua-48x48.jpg?cb=1518235417 I am working in Software Verification and Validation Group in The University of Texas at Austin now, working on automatic program repair. We build tools to automatically repair faulty programs. Before that, I worked in Software Evolution and Analysis Lab (SEAL) in The University of Texas at Austin, working on automatic program transformation and static analysis. I had an internship in Paypal Risk Data Science Group working on a Machine Learning platform, I worked on an automatic decision support system in Schlumberger, Oil Service Company in Houston in 2013, and I worked in Morgan Stanley algorithm group for automatic stock trading system. Personally, I am an zealot in software desi... https://cdn.slidesharecdn.com/ss_thumbnails/edsketchspin17-170714224032-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/edsketch-executiondriven-sketching-for-java/77890946 EdSketch: Execution-Dr... https://cdn.slidesharecdn.com/ss_thumbnails/sparkoverview-140729190732-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds LisaHua/spark-overview-37479609 Spark overview https://cdn.slidesharecdn.com/ss_thumbnails/5xufqw0usfilfr6rlbeb-signature-0ce7629d67ee9301560393cf10c0eb5e8300a0007fca4787601a03a2a645340a-poli-140729190307-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/shifu-plugintrainer-andpmmladapter/37479527 Shifu plugin-trainer a...