Code Coverage & Continuous Integration
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Seisio: a Fast, Efficient Geophysical Data Architecture for the Julia
1 SeisIO: a fast, efficient geophysical data architecture for 2 the Julia language 1∗ 2 2 2 3 Joshua P. Jones , Kurama Okubo , Tim Clements , and Marine A. Denolle 1 4 4509 NE Sumner St., Portland, OR, USA 2 5 Department of Earth and Planetary Sciences, Harvard University, MA, USA ∗ 6 Corresponding author: Joshua P. Jones ([email protected]) 1 7 Abstract 8 SeisIO for the Julia language is a new geophysical data framework that combines the intuitive 9 syntax of a high-level language with performance comparable to FORTRAN or C. Benchmark 10 comparisons with recent versions of popular programs for seismic data download and analysis 11 demonstrate significant improvements in file read speed and orders-of-magnitude improvements 12 in memory overhead. Because the Julia language natively supports parallel computing with an 13 intuitive syntax, we benchmark test parallel download and processing of multi-week segments of 14 contiguous data from two sets of 10 broadband seismic stations, and find that SeisIO outperforms 15 two popular Python-based tools for data downloads. The current capabilities of SeisIO include file 16 read support for several geophysical data formats, online data access using FDSN web services, 17 IRIS web services, and SeisComP SeedLink, with optimized versions of several common data 18 processing operations. Tutorial notebooks and extensive documentation are available to improve 19 the user experience (UX). As an accessible example of performant scientific computing for the 20 next generation of researchers, SeisIO offers ease of use and rapid learning without sacrificing 21 computational performance. 2 22 1 Introduction 23 The dramatic growth in the volume of collected geophysical data has the potential to lead to 24 tremendous advances in the science (https://ds.iris.edu/data/distribution/). -
Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK
1 From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK Abstract—This paper1 describes some of the challenges and research questions that target the most productive intersection opportunities when deploying static and dynamic analysis at we have yet witnessed: that between exciting, intellectually scale, drawing on the authors’ experience with the Infer and challenging science, and real-world deployment impact. Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, Many industrialists have perhaps tended to regard it unlikely impacting billions of people worldwide. that much academic work will prove relevant to their most The paper identifies open problems that have yet to receive pressing industrial concerns. On the other hand, it is not significant attention from the scientific community, yet which uncommon for academic and scientific researchers to believe have potential for profound real world impact, formulating these that most of the problems faced by industrialists are either as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects. boring, tedious or scientifically uninteresting. This sociological phenomenon has led to a great deal of miscommunication between the academic and industrial sectors. I. INTRODUCTION We hope that we can make a small contribution by focusing on the intersection of challenging and interesting scientific How do we transition research on static and dynamic problems with pressing industrial deployment needs. Our aim analysis techniques from the testing and verification research is to move the debate beyond relatively unhelpful observations communities to industrial practice? Many have asked this we have typically encountered in, for example, conference question, and others related to it. -
Addresssanitizer + Code Coverage Kostya Serebryany, Google Eurollvm 2014 New and Shiny -Fprofile-Instr-Generate
AddressSanitizer + Code Coverage Kostya Serebryany, Google EuroLLVM 2014 New and shiny -fprofile-instr-generate ● Coming this year ● Fast BB-level code coverage ● Increment a counter per every (*) BB ○ Possible contention on counters ● Creates special non-code sections ○ Counters ○ Function names, line numbers Meanwhile: ASanCoverage ● Tiny prototype-ish thing: ○ Part of AddressSanitizer ○ 30 lines in LLVM, 100 in run-time ● Function- or BB- level coverage ○ Booleans only, not counters ○ No contention ○ No extra sections in the binary At compile time: if (!*BB_Guard) { __sanitizer_cov(); *BB_Guard = 1; } At run time void __sanitizer_cov() { Record(GET_CALLER_PC()); } At exit time ● For every binary/DSO in the process: ○ Dump observed PCs in a separate file as 4-byte offsets At analysis time ● Compare/Merge using 20 lines of python ● Symbolize using regular DWARF % cat cov.c int main() { } % clang -g -fsanitize=address -mllvm -asan-coverage=1 cov. c % ASAN_OPTIONS=coverage=1 ./a.out % wc -c *sancov 4 a.out.15751.sancov % sancov.py print a.out.15751.sancov sancov.py: read 1 PCs from a.out.15751.sancov sancov.py: 1 files merged; 1 PCs total 0x4850b7 % sancov.py print *.sancov | llvm-symbolizer --obj=a.out main /tmp/cov.c:1:0 Fuzzing with coverage feedback ● Test corpus: N random tests ● Randomly mutate random test ○ If new BB is covered -- add this test to the corpus ● Many new bugs in well fuzzed projects! Feedback from our customers ● Speed is paramount ● Binary size is important ○ Permanent & temporary storage, tmps, I/O ○ Stripping non-code -
Empirical Study of Restarted and Flaky Builds on Travis CI
Empirical Study of Restarted and Flaky Builds on Travis CI Thomas Durieux Claire Le Goues INESC-ID and IST, University of Lisbon, Portugal Carnegie Mellon University [email protected] [email protected] Michael Hilton Rui Abreu Carnegie Mellon University INESC-ID and IST, University of Lisbon, Portugal [email protected] [email protected] ABSTRACT Continuous integration is an automatic process that typically Continuous Integration (CI) is a development practice where devel- executes the test suite, may further analyze code quality using static opers frequently integrate code into a common codebase. After the analyzers, and can automatically deploy new software versions. code is integrated, the CI server runs a test suite and other tools to It is generally triggered for each new software change, or at a produce a set of reports (e.g., the output of linters and tests). If the regular interval, such as daily. When a CI build fails, the developer is result of a CI test run is unexpected, developers have the option notified, and they typically debug the failing build. Once the reason to manually restart the build, re-running the same test suite on for failure is identified, the developer can address the problem, the same code; this can reveal build flakiness, if the restarted build generally by modifying or revoking the code changes that triggered outcome differs from the original build. the CI process in the first place. In this study, we analyze restarted builds, flaky builds, and their However, there are situations in which the CI outcome is unex- impact on the development workflow. -
Usage, Costs, and Benefits of Continuous Integration in Open
Usage, Costs, and Benefits of Continuous Integration in Open-Source Projects Michael Hilton Timothy Tunnell Kai Huang Oregon State University University of Illinois at University of Illinois at [email protected] Urbana-Champaign Urbana-Champaign [email protected] [email protected] Darko Marinov Danny Dig University of Illinois at Oregon State University Urbana-Champaign [email protected] [email protected] ABSTRACT CI using several quality metrics. However, the study does Continuous integration (CI) systems automate the compi- not present any detailed information about the use of CI. lation, building, and testing of software. Despite CI rising In fact, despite some folkloric evidence about the use of CI, as a big success story in automated software engineering, it there is no systematic study about CI systems. has received almost no attention from the research commu- Not only do we lack basic knowledge about the extent nity. For example, how widely is CI used in practice, and to which open-source projects are adopting CI, but also what are some costs and benefits associated with CI? With- we have no answers to many other important questions re- out answering such questions, developers, tool builders, and lated to CI. What are the costs of CI? Does CI deliver researchers make decisions based on folklore instead of data. on the promised benefits, such as releasing more often, or In this paper, we use three complementary methods to helping make changes (e.g., to merge pull requests) faster? study in-depth the usage of CI in open-source projects. To Are developers maximizing the usage of CI? Despite the understand what CI systems developers use, we analyzed widespread popularity of CI, we have very little quantita- 34,544 open-source projects from GitHub. -
A Zero Kernel Operating System: Rethinking Microkernel Design by Leveraging Tagged Architectures and Memory-Safe Languages
A Zero Kernel Operating System: Rethinking Microkernel Design by Leveraging Tagged Architectures and Memory-Safe Languages by Justin Restivo B.S., Massachusetts Institute of Technology (2019) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Masters of Engineering in Computer Science and Engineering at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2020 © Massachusetts Institute of Technology 2020. All rights reserved. Author............................................................................ Department of Electrical Engineering and Computer Science January 29, 2020 Certified by....................................................................... Dr. Howard Shrobe Principal Research Scientist, MIT CSAIL Thesis Supervisor Certified by....................................................................... Dr. Hamed Okhravi Senior Staff Member, MIT Lincoln Laboratory Thesis Supervisor Certified by....................................................................... Dr. Samuel Jero Technical Staff, MIT Lincoln Laboratory Thesis Supervisor Accepted by...................................................................... Katrina LaCurts Chair, Master of Engineering Thesis Committee DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the Assistant Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions -
Guidelines on Minimum Standards for Developer Verification of Software
Guidelines on Minimum Standards for Developer Verification of Software Paul E. Black Barbara Guttman Vadim Okun Software and Systems Division Information Technology Laboratory July 2021 Abstract Executive Order (EO) 14028, Improving the Nation’s Cybersecurity, 12 May 2021, di- rects the National Institute of Standards and Technology (NIST) to recommend minimum standards for software testing within 60 days. This document describes eleven recommen- dations for software verification techniques as well as providing supplemental information about the techniques and references for further information. It recommends the following techniques: • Threat modeling to look for design-level security issues • Automated testing for consistency and to minimize human effort • Static code scanning to look for top bugs • Heuristic tools to look for possible hardcoded secrets • Use of built-in checks and protections • “Black box” test cases • Code-based structural test cases • Historical test cases • Fuzzing • Web app scanners, if applicable • Address included code (libraries, packages, services) The document does not address the totality of software verification, but instead recom- mends techniques that are broadly applicable and form the minimum standards. The document was developed by NIST in consultation with the National Security Agency. Additionally, we received input from numerous outside organizations through papers sub- mitted to a NIST workshop on the Executive Order held in early June, 2021 and discussion at the workshop as well as follow up with several of the submitters. Keywords software assurance; verification; testing; static analysis; fuzzing; code review; software security. Disclaimer Any mention of commercial products or reference to commercial organizations is for infor- mation only; it does not imply recommendation or endorsement by NIST, nor is it intended to imply that the products mentioned are necessarily the best available for the purpose. -
The Impact of Software Evolution on Code Coverage Information
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Computer Science and Engineering, Department CSE Conference and Workshop Papers of 2001 The Impact of Software Evolution on Code Coverage Information Sebastian Elbaum University of Nebraska-Lincoln, [email protected] David Gable University of Nebraska-Lincoln, [email protected] Gregg Rothermel University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/cseconfwork Part of the Computer Sciences Commons Elbaum, Sebastian; Gable, David; and Rothermel, Gregg, "The Impact of Software Evolution on Code Coverage Information" (2001). CSE Conference and Workshop Papers. 132. https://digitalcommons.unl.edu/cseconfwork/132 This Article is brought to you for free and open access by the Computer Science and Engineering, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in CSE Conference and Workshop Papers by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Proceedings. IEEE International Conference on Software Maintenance, 2001. Digital Object Identifier: 10.1109/ICSM.2001.972727 Publication Year: 2001 , Page(s): 170 - 179 The Impact of Software Evolution on Code Coverage Information Sebastian Elbaum David Gable Gregg Rothermel Dept. of Computer Dept. of Computer Computer Science Dept. Science and Engineering Science and Engineering Oregon State University University of Nebraska-Lincoln University of Nebraska-Lincoln Corvallis, OR Lincoln, Nebraska Lincoln, Nebraska [email protected] [email protected] [email protected] Abstract Often, code coverage information is collected for a ver- sion of a program to aid in some maintenance or testing task Many tools and techniques for addressing software performed on that particular version. -
General Commands Reference Guide C
General Commands Reference Guide C TRACE32 Online Help TRACE32 Directory TRACE32 Index TRACE32 Documents ...................................................................................................................... General Commands ...................................................................................................................... General Commands Reference Guide C .................................................................................. 1 History ...................................................................................................................................... 11 CACHE ...................................................................................................................................... 12 CACHE View and modify CPU cache contents 12 CACHE.CLEAN Clean CACHE 12 CACHE.ComPare Compare CACHE with memory 13 CACHE.DUMP Dump CACHE 14 CACHE.FLUSH Clean and invalidate CACHE 15 CACHE.GET Get CACHE contents 16 CACHE.INFO View all information related to an address 16 CACHE.INVALIDATE Invalidate CACHE 17 CACHE.List List CACHE contents 17 CACHE.ListFunc List cached functions 18 CACHE.ListLine List cached source code lines 19 CACHE.ListModule List cached modules 19 CACHE.ListVar List cached variables 20 CACHE.LOAD Load previously stored cache contents 21 CACHE.RELOAD Reload previously loaded cache contents 21 CACHE.SAVE Save cache contents for postprocessing 21 CACHE.SNAPSHOT Take cache snapshot for comparison 22 CACHE.UNLOAD Unload previously loaded cache contents 23 CACHE.view -
Spice Program Open Source 2015 Learnings
Spice Program Open Source 2015 learnings Please see this Futurice Spice Program blog post to learn the context for this list. Learned even more about practical problems in measuring time. Direct uploads from browser to AWS S3. Basics of Polymer. React + Redux (lots of it). Basic info about Google Cloud Services. Practical usage of Google Cloud Storage (the Google's S3 equivalent). Heroku Pipelines. Improved & more modular Angular application structure formation. Webpack, JSON Web Token implementation on the backend, and ES6. Actually learning how Promises should be used in JavaScript. Some knowledge about Dokku, the hostityourselfHeroku. Got practical experience and more understanding about Node.js, npm, and bacon.js. How the Azure Web App continuous deployment works. A bit on how Travis CI operates under the hood. How to make Joomla templates. Learned about Let's Encrypt on Apache with vhosts. Learned more about Web MIDI API and RxJS. Also more about ES6 and setting up a compact development environment for that. Learned basics of RxJS. Learned a lot about creating a reusable library with JS/browserify. Learned some Cycle.js. Learned a little about Node.js testing. Learned the EMV chip card specification on very detailed level by implementing large part of the specification. I'm using the EMV knowledge on daily basis in my current client project. Improved my Clojure language skills. Learned to work with the Go programming language. Wrote an OSS tool that I later then used in a customer project. I learned better ways to use Dagger 2 in Android and trial and evaluate other app architectural patterns based around this. -
Open Source Best Practices: from Continuous Integration to Static Linters
THE MOLECULAR SCIENCES SOFTWARE INSTITUTE - LOGO CONCEPTS ROUND 2 Open Source Best Practices: From Continuous Integration to Static Linters Daniel G. A. Smith and Ben Pritchard The Molecular Sciences Software Institute !1 @dga_smith / @molssi_nsf 1. 3 The Molecular Sciences Software Institute (MolSSI) •Designed to serve and enhance the software development efforts of the broad field of computational molecular science (CMS). •Funded at the institute level under the Software Infrastructure for Sustained Innovation (SI2) initiative by the National Science Foundation. •Launched August 1st, 2016 •Collaborative effort by: •Virginia Tech •U.C. Berkeley •U. Southern •Rice U. •Stanford U. California •Stony Brook U. •Rutgers U. •Iowa State U. !2 The Molecular Sciences Software Institute (MolSSI) •Software Infrastructure •Build open-source frameworks and packages for the entire CMS community •Education and training •Online materials and training guides •Host a number of targeted workshops, summer schools, and other training programs •Community Engagement and Leadership •Interoperability standards and best practices !3 •Data and API standardization targets Talk Motivation •Many terms thrown around: •Best Practice •Manage Code •DevOps •Build and Package •Open Source •“Just get the physics •Software Sustainability working” •“Incorrect Software” “Best” is subjective and depends on your own community. Lets discuss practical applications and how to make our lives easier! !4 Open Source Open Source software is software that can be freely accessed, used, -
Non-Intrusive Structural Coverage for Objective Caml
Electronic Notes in Theoretical Computer Science 264 (4) (2011) 59–73 www.elsevier.com/locate/entcs Non-Intrusive Structural Coverage for Objective Caml Philippe Wang1, Adrien Jonquet2, Emmanuel Chailloux3 Equipe´ APR Laboratoire d’Informatique de Paris 6 (CNRS UMR 7606) Universit´e Pierre et Marie Curie (Paris 6) 4 place Jussieu, 75005 Paris, France Abstract This paper presents a non-intrusive method for Objective Caml code coverage analysis. While classic methods rewrite the source code to an instrumented version that will produce traces at runtime, our approach chooses not to rewrite the source code. Instead, we use a virtual machine to monitor instructions execution and produce traces. These low-level traces are used to create a machine code coverage report. Combined with control-flow debug information, they can be analyzed to produce a source code coverage report. The purpose of this approach is to make available a method to generate code coverage analysis with the same binary for testing and for production. Our customized virtual machine respects the same semantics as the original virtual machine; one of its original aspects is that it is implemented in the Objective Caml, the very language we build the tool for. This work is part of the Coverage project, which aims to develop open source tools for safety-critical embedded applications and their code generators. Keywords: Certification Tools Design, Code Coverage, Objective Caml Virtual Machine 1 Introduction One of the most demanding development process for safety-critical software was defined a couple of decades ago by the civil avionics authorities as the DO-178B standard [17].