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MOAD: Modeling Observation-Based Approximate Dependency

MOAD: Modeling Observation-Based Approximate Dependency

引用

ieee International Workshop on source code analysis and manipulation

作者： Seongmin Lee David Binkley Robert Feldt Nicolas Gold Shin Yoo KAIST Daejeon Republic of Korea Loyola University Maryland Baltimore United States Chalmers University of Technology Gothenburg Sweden University College London London United Kingdom

While dependency analysis is foundational to many applications of program analysis, the static nature of many existing techniques presents challenges such as limited scalability and inability to cope with multi-lingual systems. We present a novel dependency analysis technique that aims to approximate program dependency from a relatively small number of perturbed executions. Our technique, called MOAD (Modeling Observation-based Approximate Dependency), reformulates program dependency as the likelihood that one program element is dependent on another, instead of a more classical Boolean relationship. MOAD generates a set of program variants by deleting parts of the source code, and executes them while observing the impacts of the deletions on various program points. From these observations, MOAD infers a model of program dependency that captures the dependency relationship between the modification and observation points. While MOAD is a purely dynamic dependency analysis technique similar to Observation Based Slicing (ORBS), it does not require iterative deletions. Rather, MOAD makes a much smaller number of multiple, independent observations in parallel and infers dependency relationships for multiple program elements simultaneously, significantly reducing the cost of dynamic dependency analysis. We evaluate MOAD by instantiating program slices from the obtained probabilistic dependency model. Compared to ORBS, MOAD's model construction requires only 18.7% of the observations used by ORBS, while its slices are only 16% larger than the corresponding ORBS slice, on average.

关键词： Analytical models Probabilistic logic Inference algorithms Semantics Performance analysis Tools Trajectory

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Toward Optimal Selection of Information Retrieval Models for Software Engineering Tasks

Toward Optimal Selection of Information Retrieval Models for...

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ieee International Workshop on source code analysis and manipulation

作者： Md Masudur Rahman Saikat Chakraborty Gail Kaiser Baishakhi Ray Department of Computer Science Purdue University West Lafayette IN US Department of Computer Science Columbia University New York NY US

Information Retrieval (IR) plays a pivotal role in diverse Software Engineering (SE) tasks, e.g., bug localization and triaging, bug report routing, code retrieval, requirements analysis, etc. SE tasks operate on diverse types of documents including code, text, stack-traces, and structured, semi-structured and unstructured meta-data that often contain specialized vocabularies. As the performance of any IR-based tool critically depends on the underlying document types, and given the diversity of SE corpora, it is essential to understand which models work best for which types of SE documents and tasks. We empirically investigate the interaction between IR models and document types for two representative SE tasks (bug localization and relevant project search), carefully chosen as they require a diverse set of SE artifacts (mixtures of code and text), and confirm that the models' performance varies significantly with mix of document types. Leveraging this insight, we propose a generalized framework, SRCH, to automatically select the most favorable IR model(s) for a given SE task. We evaluate SRCH w.r.t. these two tasks and confirm its effectiveness. Our preliminary user study shows that SRCH's intelligent adaption of the IR model(s) to the task at hand not only improves precision and recall for SE tasks but may also improve users' satisfaction.

关键词： Computer bugs Task analysis Large scale integration Tools Tuning Software Computational modeling

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Characterizing Leveraged Stack Overflow Posts

Characterizing Leveraged Stack Overflow Posts

引用

ieee International Workshop on source code analysis and manipulation

作者： Salvatore Geremia Gabriele Bavota Rocco Oliveto Michele Lanza Massimiliano Di Penta University of Molise Universita??? della Svizzera italiana (USI) University of Sannio

Stack Overflow is the most popular question and answer website on computer programming with more than 2.5M users, 16M questions, and a new answer posted, on average, every five seconds. This wide availability of data led researchers to develop techniques to mine Stack Overflow posts. The aim is to find and recommend posts with information useful to developers. However, and not surprisingly, not every Stack Overflow post is useful from a developer's perspective. We empirically investigate what the characteristics of "useful" Stack Overflow posts are. The underlying assumption of our study is that posts that were used (referenced in the source code) in the past by developers are likely to be useful. We refer to these posts as leveraged posts. We study the characteristics of leveraged posts as opposed to the non-leveraged ones, focusing on community aspects (e.g., the reputation of the user who authored the post), the quality of the included code snippets (e.g., complexity), and the quality of the post's textual content (e.g., readability). Then, we use these features to build a prediction model to automatically identify posts that are likely to be leveraged by developers. Results of the study indicate that post meta-data (e.g., the number of comments received by the answer) is particularly useful to predict whether it has been leveraged or not, whereas code readability appears to be less useful. A classifier can classify leveraged posts with a precision of 65% and recall of 49% and non-leveraged ones with a precision of 95% and recall of 97%. This opens the road towards an automatic identification of "high-quality content" in Stack Overflow.

关键词： Java Complexity theory Recommender systems Programming Software Documentation Task analysis

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analysis and Implementation of a Parallel Computing Cluster for Solving Computational Problems in Data Analytics

Analysis and Implementation of a Parallel Computing Cluster ...

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ieee International conference on Computing, Communication and Security (ICCCS)

作者： Chavala Mutyala Rao K. Shyamala University College of Engineering Osmania University Hyderabad

ISBN: (数字)9781728191805

ISBN: (纸本)9781728191812

As computers get updated by a couple of generations, they get disposed of as useless but when a large number of such computers are put together and they share each other's computational power, they can get really powerfulcluster defines as a parallel computing system which is inter connected with a group of independent computers working togeather as a single coherent system. Clusters uses message passing interface for communication, and code snippets have to be explicitly programmed to run on underlying hardware. More computing nodes to be added to the cluster based on the need. While designing parallel cluster in many cases we opt for open source technologies and Linux system components which reduce software costs. Parallel Cluster is a way of connecting similar computers together and can get a task done by sharing the task load. The task is shared in coordination of the master and slave nodes in a cluster. Parallel computing clusters allows group of computers with different hardware configurations, to work together to provide solutions to different computational problems. The resources like memory, storage and CPU cores in a cluster computing environment are integrated into one system. This paper is to set up a cluster environment for academic purposes and provide a huge computation power to run a program in parallel mode this will reduce the time taken to execute a program. A parallel code is always better then sequential code in terms of time taken to run.

关键词： Rocks Servers Task analysis Parallel processing Linux Hardware Performance evaluation

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A simulated environment for early development stages of reinforcement learning algorithms for closed-loop deep brain stimulation 41

A simulated environment for early development stages of rein...

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41st Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Castano-Candamil, Sebastian Vaihinger, Mara Tangermann, Michael Univ Freiburg Dept Comp Sci Brain State Decoding Lab BrainLinks BrainTools Freiburg Germany Univ Freiburg Dept Comp Sci Autonomous Intelligent Syst Lab Freiburg Germany

ISBN: (纸本)9781538613115

In recent years, closed-loop adaptive deep brain stimulation (aDBS) for Parkinson's disease (PD) has gained focus in the research community, due to promising proof-of-concept studies showing its suitability for improving DBS therapy and ameliorating related side effects. The main challenges faced in the aDBS control problem is the presence of non-stationary/non-linear dynamics and the heterogeneity of PD's phenotype, making the exploration of data-driven dynamics-aware control algorithms a promising research direction. However, due to the severe safety constraints related to working with patients, aDBS is a sensitive research field that requires surrogate development platforms with growing complexity, as novel control algorithms are validated. With our current contribution, we propose the characterization and categorization of non-stationary dynamics found in the aDBS problem. We show how knowledge about these dynamics can be embedded in a surrogate simulation environment, which has been designed to support early development stages of aDBS control strategies, specifically those based on reinforcement learning (RL) algorithms. Finally, we present a comparison of representative RL methods designed to cope with the type of non-stationary dynamics found in aDBS. To allow reproducibility and encourage adoption of our approach, the source code of the developed methods and simulation environment are made available online.

关键词： Satellite broadcasting Heuristic algorithms Gravity Training Task analysis Standards Reinforcement learning

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Exploratory study of slack Q&A chats as a mining source for software engineering tools 16

Exploratory study of slack Q&A chats as a mining source for ...

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16th ieee/ACM International conference on Mining Software Repositories, MSR 2019

作者： Chatterjee, Preetha Damevski, Kostadin Pollock, Lori Augustine, Vinay Kraft, Nicholas A. University of Delaware NewarkDE United States Virginia Commonwealth University RichmondVA United States ABB Corporate Research RaleighNC United States

ISBN: (纸本)9781728134123

Modern software development communities are increasingly social. Popular chat platforms such as Slack host public chat communities that focus on specific development topics such as Python or Ruby-on-Rails. Conversations in these public chats often follow a Q&A format, with someone seeking information and others providing answers in chat form. In this paper, we describe an exploratory study into the potential use-fulness and challenges of mining developer Q&A conversations for supporting software maintenance and evolution tools. We designed the study to investigate the availability of information that has been successfully mined from other developer communications, particularly Stack Overflow. We also analyze characteristics of chat conversations that might inhibit accurate automated analysis. Our results indicate the prevalence of useful information, including API mentions and code snippets with descriptions, and several hurdles that need to be overcome to automate mining that information. © 2019 ieee.

关键词： Software design

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A large-scale study about quality and reproducibility of jupyter notebooks 16

A large-scale study about quality and reproducibility of jup...

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16th ieee/ACM International conference on Mining Software Repositories, MSR 2019

作者： Pimentel, Joao Felipe Murta, Leonardo Braganholo, Vanessa Freire, Juliana Universidade Federal Fluminense Niterói Brazil New York University New York United States

ISBN: (纸本)9781728134123

Jupyter Notebooks have been widely adopted by many different communities, both in science and industry. They support the creation of literate programming documents that combine code, text, and execution results with visualizations and all sorts of rich media. The self-documenting aspects and the ability to reproduce results have been touted as significant benefits of notebooks. At the same time, there has been growing criticism that the way notebooks are being used leads to unexpected behavior, encourage poor coding practices, and that their results can be hard to reproduce. To understand good and bad practices used in the development of real notebooks, we studied 1.4 million notebooks from GitHub. We present a detailed analysis of their characteristics that impact reproducibility. We also propose a set of best practices that can improve the rate of reproducibility and discuss open challenges that require further research and development. © 2019 ieee.

关键词： data analysis data visualisation document handling Internet notebook computers public domain software software quality hand-held equipment electronic document handling open source software Notebooks Data visualisation software quality Internets reproducibility literate programming Data analysis

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CROP: Linking code Reviews to source code Changes 15

CROP: Linking Code Reviews to Source Code Changes

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ACM/ieee 15th International conference on Mining Software Repositories (MSR)

作者： Paixao, Matheus Krinke, Jens Han, Donggyun Harman, Mark UCL London England Facebook London England

ISBN: (纸本)9781450357166

code review has been widely adopted by both industrial and open source software development communities. Research in code review is highly dependant on real-world data, and although existing researchers have attempted to provide code review datasets, there is still no dataset that links code reviews with complete versions of the system's code base mainly because reviewed versions are not kept in the system's version control repository. Thus, we present CROP, the code Review Open Platform, the first curated code review repository that links review data with isolated complete versions (snapshots) of the source code at the time of review. CROP currently provides data for 8 software systems, 48,975 reviews and 112,617 patches, including versions of the systems that are inaccessible in the systems' original repositories. Moreover, CROP is extensible, and it will be continuously curated and extended.

关键词： code Review Repository Platform Software Change analysis

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Multimodal Sensor Fusion with Differentiable Filters

Multimodal Sensor Fusion with Differentiable Filters

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2020 ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Michelle A. Lee Brent Yi Roberto Martín-Martín Silvio Savarese Jeannette Bohg Stanford Artificial Intelligence Lab (SAIL) Stanford University

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Leveraging multimodal information with recursive Bayesian filters improves performance and robustness of state estimation, as recursive filters can combine different modalities according to their uncertainties. Prior work has studied how to optimally fuse different sensor modalities with analytical state estimation algorithms. However, deriving the dynamics and measurement models along with their noise profile can be difficult or lead to intractable models. Differentiable filters provide a way to learn these models end-to-end while retaining the algorithmic structure of recursive filters. This can be especially helpful when working with sensor modalities that are high dimensional and have very different characteristics. In contact-rich manipulation, we want to combine visual sensing (which gives us global information) with tactile sensing (which gives us local information). In this paper, we study new differentiable filtering architectures to fuse heterogeneous sensor information. As case studies, we evaluate three tasks: two in planar pushing (simulated and real) and one in manipulating a kinematically constrained door (simulated). In extensive evaluations, we find that differentiable filters that leverage crossmodal sensor information reach comparable accuracies to unstructured LSTM models, while presenting interpretability benefits that may be important for safety-critical systems. We also release an open-source library for creating and training differentiable Bayesian filters in PyTorch, which can be found on our project website: https://***/view/multimodalfilter.

关键词： Visualization Heuristic algorithms Filtering algorithms Information filters Robot sensing systems State estimation Task analysis

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Processing Large Datasets of Fined Grained source code Changes

Processing Large Datasets of Fined Grained Source Code Chang...

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International conference on Software Maintenance (ICSM)

作者： Stanislav Levin Amiram Yehudai The Blavatnik School of Computer Science Tel Aviv University Tel-Aviv Israel

In the era of Big code, when researchers seek to study an increasingly large number of repositories to support their findings, the data processing stage may require manipulating millions and more of records. In this work we focus on studies involving fine-grained AST level source code changes. We present how we extended the codeDistillery source code mining framework with data manipulation capabilities, aimed to alleviate the processing of large datasets of fine grained source code changes. The capabilities we have introduced allow researchers to highly automate their repository mining process and streamline the data acquisition and processing phases. These capabilities have been successfully used to conduct a number of studies, in the course of which dozens of millions of fine-grained source code changes have been processed.

关键词： Sparks Data mining Software Task analysis Tools Data structures Computer science

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