As worldwide capability to collect, store, and manage information continues to grow, the resulting datasets become increasingly difficult to understand and extract insights from. Interactive data visualizations offers a promising avenue to efficiently navigate and gain insights from highly complex datasets, but the velocity of modern data streams often means that precomputed representations or summarizations of the data will quickly become obsolete. Our system, Agami, provides live-updating, interactive visualizations over streaming data. We leverage in-memory data sketches to summarize and aggregate information to be visualized, and also allow users to query future feature values by leveraging online machine learning models. Our approach facilitates low-latency, iterative exploration of data streams and can scale out incrementally to handle increasing stream velocities and query loads. We provide a thorough evaluation of our data structures and system performance using a real-world meteorological dataset.

Electronic documents are widely used to store and share information such as bank statements, contracts, articles, maps and tax information. Many different applications exist for displaying a given electronic document, and users rightfully assume that documents will be rendered similarly independently of the application used. However, this is not always the case, and these inconsistencies, regardless of their causes—bugs in the application or the file itself—can become critical sources of miscommunication. In this paper, we present a study on the correctness of PDF documents and readers. We start by manually investigating a large number of real-world PDF documents to understand the frequency and characteristics of cross-reader inconsistencies, and find that such inconsistencies are common—13.5% PDF files are inconsistently rendered by at least one popular reader. We then propose an approach to detect and localize the source of such inconsistencies automatically. We evaluate our automatic approach on a large corpus of over 230 K documents using 11 popular readers and our experiments have detected 30 unique bugs in these readers and files. We also reported 33 bugs, some of which have already been confirmed or fixed by developers.

We advocate for a paradigm shift in supporting the information needs of developers, centered around the concept of automated on-demand developer documentation. Currently, developer information needs are fulfilled by asking experts or consulting documentation. Unfortunately, traditional documentation practices are inefficient because of, among others, the manual nature of its creation and the gap between the creators and consumers. We discuss the major challenges we face in realizing such a paradigm shift, highlight existing research that can be leveraged to this end, and promote opportunities for increased convergence in research on software documentation.

We present LittleTable, a relational database that Cisco Meraki has used since 2008 to store usage statistics, event logs, and other time-series data from our customers' devices.

We propose and implement a new approach, Document-Assisted Symbolic Execution (DASE), to improve automated test generation and bug detection. DASE leverages natural language processing techniques and heuristics to analyze program documentation to extract input constraints automatically. DASE then uses the input constraints to guide symbolic execution to focus on inputs that are semantically more important.We evaluated DASE on 88 programs from 5 mature real-world software suites: COREUTILS, FINDUTILS, GREP, BINUTILS, and ELFTOOLCHAIN. DASE detected 12 previously unknown bugs that symbolic execution without input constraints failed to detect, 6 of which have already been confirmed by the developers. In addition, DASE increases line coverage, branch coverage, and call coverage by 14.2 -- 120.3%, 2.3 -- 167.7%, and 16.9 -- 135.2% respectively, which are 6.0 -- 21.1 percentage points (pp), 1.6 -- 18.9 pp, and 2.8 -- 20.1 pp increases. The accuracies of input constraint extraction are 97.8 -- 100%.

Meraki is a cloud-based network management system which provides centralized configuration, monitoring, and network troubleshooting tools across hundreds of thousands of sites worldwide. As part of its architecture, the Meraki system has built a database of time-series measurements of wireless link, client, and application behavior for monitoring and debugging purposes. This paper studies an anonymized subset of measurements, containing data from approximately ten thousand radio access points, tens of thousands of links, and 5.6 million clients from one-week periods in January 2014 and January 2015 to provide a deeper understanding of real-world network behavior. This paper observes the following phenomena: wireless network usage continues to grow quickly, driven most by growth in the number of devices connecting to each network. Intermediate link delivery rates are common indoors across a wide range of deployment environments. Typical access points share spectrum with dozens of nearby networks, but the presence of a network on a channel does not predict channel utilization. Most access points see 2.4 GHz channel utilization of 20% or more, with the top decile seeing greater than 50%, and the majority of the channel use contains decodable 802.11 headers.

Code comments are an integral part of software development. They improve program comprehension and software maintainability. The lack of code comments is a common problem in the software industry. Therefore, it is beneficial to generate code comments automatically. In this paper, we propose a general approach to generate code comments automatically by analyzing existing software repositories. We apply code clone detection techniques to discover similar code segments and use the comments from some code segments to describe the other similar code segments. We leverage natural language processing techniques to select relevant comment sentences. In our evaluation, we analyze 42 million lines of code from 1,005 open source projects from GitHub, and use them to generate 359 code comments for 21 Java projects. We manually evaluate the generated code comments and find that only 23.7% of the generated code comments are good. We report to the developers the good code comments, whose code segments do not have an existing code comment. Amongst the reported code comments, seven have been confirmed by the developers as good and committable to the software repository while the rest await for developers' confirmation. Although our approach can generate good and committable comments, we still have to improve the yield and accuracy of the proposed approach before it can be used in practice with full automation.

Code comments improve software maintainability. To address the comment scarcity issue, we propose a new automatic comment generation approach, which mines comments from a large programming Question and Answer (Q&A) site. Q&A sites allow programmers to post questions and receive solutions, which contain code segments together with their descriptions, referred to as code-description mappings.We develop AutoComment to extract such mappings, and leverage them to generate description comments automatically for similar code segments matched in open-source projects. We apply AutoComment to analyze Java and Android tagged Q&A posts to extract 132,767 code-description mappings, which help AutoComment to generate 102 comments automatically for 23 Java and Android projects. The user study results show that the majority of the participants consider the generated comments accurate, adequate, concise, and useful in helping them understand the code.

Algorithm implementation efficiency is key to delivering high-performance computing capabilities to demanding, high throughput signal and image processing applications and simulations. Significant progress has been made in optimization of serial programs, but many applications require parallel processing, which brings with it the difficult task of determining efficient mappings of algorithms. The pMapper infrastructure addresses the problem of performance optimization of multistage MATLABreg applications on parallel architectures. pMapper is an automatic performance tuning library written as a layer on top of pMatlab: Parallel Matlab Toolbox. While pMatlab abstracts the message-passing interface, the responsibility of mapping numerical arrays falls on the user. Choosing the best mapping for a set of numerical arrays is a nontrivial task that requires significant knowledge of programming languages, parallel computing, and processor architecture. pMapper automates the task of map generation. This abstract addresses the design details of pMapper and presents preliminary results