Appendix a – Checklist: Sanity Check on the Design

Total Page:16

File Type:pdf, Size:1020Kb

Appendix a – Checklist: Sanity Check on the Design Appendix A – Checklist: Sanity Check on the Design A.1 Conclusion Conclusion Y / N The specifications (test base) describe the system clearly and precisely. They are therefore sufficient as the basis for a structured test project. If the conclusion is negative: High-level errors that were recorded during the sanity check are dis- played below. For each error, indicate the risks for the test design and the measures needed to rectify them. Error Risk Measure 358 Appendix A A.2 Result Description Y/N Not applicable Solution The functionality, the processes and their coherence is described sufficiently The functionality, the processes and their coherence concur with the anticipated goal All of the relevant quality requirements have been sufficiently taken care of The result of the risk analysis can be traced in the test base A.3 Control Description Y/N Not applicable Solution Contains history log (including version administration) Contains approval/distribution list Contains document description (including author) The document has a status Contains reference list A.4 Structure Description Y/N Not applicable Solution Contains executive (or management) summary Contains table of contents (current) Contains introduction Contains chapter(s) with an overview or description of the functionality (and related screens/reports and messages) Contains chapters with data models and data descriptions Contains chapters with functional specifications Contains chapters with non-functional specifications Contains chapters with interface specifications Appendix A 359 A.5 Content Concerned functionality Y/N Not applicable Solution Contains system overview • System description • Purpose of the system, what is the use for the users and how does it support their business processes (high level) • Scope diagram/system diagram • System environment and dependencies • System owners, both functional and technical • Sites where the system will be implemented The system contains various subsystems • For every subsystem: a short description with basic functionality, processes and related data • Boundaries of and between subsystems • Relations between subsystems Quality attributes • Quality attributes are defined in the functional descriptions. • Relevant quality attributes (e. g. performance, security) are defined for the whole system. • Relevant quality attributes (e. g. performance, security) are defined for each subsystem • Quality attributes are defined in functional descriptions. • Contains workflow/process flow/state transition or other diagrams • Description • Lists actions, conditions and situations • Describes actions, conditions and situations Contains use case (UC) • Use case description • Use case diagram (UCD) • Connection between UCD and relevant business processes • UCD contains boundaries • UCD contains primary and secondary UC 360 Appendix A Concerned functionality Y/N Not applicable Solution • Connection between UC and functionality • UC contains primary scenario, alterna- tive scenario and fallback scenario Contains screen information • Screen layout • Screen/system navigation • I/O information Contains report information • Report description • Parameters • Fields • Grouping and sorting • Report layout • Contents Data model and data description Y/N Not applicable Solution Contains graphic model (ERD) Contains description of the entities • Entity definition: a clear and unambi- guous definition of the notion of entity • Attribute definition: a clear and unam- biguous definition of every attribute of the entity, including a reference to a domain or description of a type of data, length and permitted values • Primary and foreign keys: the attributes that identify the (related) entities • Quantities: minimum, average and maximum number of entities and growth rate per period Contains description of relations • Name: of the relation and the inverse relation • Cardinality: how many entities are linked to the relations • Option: the extent to which the relation is mandatory or optional for the entities of the relation • Constraints: have the regulations been established that determine how the system should react according to the cardinality and possibilities within the relations? Appendix A 361 Data model and data description Y/N Not applicable Solution Contains description of domains • Name • Data type and length • Permitted values Contains description of constraints Contains description of regulations Contains description of stored procedures Contains description of triggers Contains description of defaults System interfaces Y/N Not applicable Solution Contains the name of the interface; for example <interface System A – System B> Contains description of the purpose Contains workflow/scheme/diagram Contains details about the process Contains description of the type of interface: operational/data/batch/online Contains detailed information about the format of the interface: structure, data elements, data types, length, permitted values Contains description of error handling Contains description of error logging Contains description of constraints Functional specifications Y/N Not applicable Solution Contains name of the function Contains purpose of the function Contains process information Contains I/O information Contains description of data processing Contains description of calculation functionality Contains description of constraints (input checks) Contains description of error handling 362 Appendix A Authorization Y/N Not applicable Solution Description of the required authorization (profiles) Contains authorization matrix Technical architecture Y/N Not applicable Solution Description and/or schemes of the infra- structure, for example, network, servers, DBMS, operating systems, middleware Description of technical aspects of interfaces Appendix B – Checklist: Sanity Check on the Testware B.1 Conclusion Conclusion Y / N The testware is described accurately enough and/or works well enough for reuse If the conclusion is negative High-level errors that were recorded during the sanity check are dis- played below. For each error, indicate the risks for the test design and the measures needed to rectify them. Error Risk Measure 364 Appendix B B.2 Result Description Y/N Not applicable Solutions The functionality that is to be tested and the processes and their coherence are sufficiently described The functionality and the processes and their coherence concur with the anticipated goal All of the relevant quality attributes have been sufficiently taken care of The outcome of the risk analysis is traceable in the test design Test results from the last time the testware was used are available and provide insight into the quality of the tests Errors that were found after last time the testware was used are available and indicate on what points the coverage of the testware is insufficient B.3 Logical tests Description Y/N Not applicable Solution The test basis on which the logical test is based is clearly specified The logical test design is up to date, there are no major outstanding issues in the design specified in the test base The structure of the test design is clear Logical tests can be traced back to related specification elements so changes in the test base can efficiently implemented Logical tests can be traced back to the related risk category in the TRA so changes in the TRA can be efficiently implemented Logical tests are set up on the basis of test techniques Where needed, test techniques are applied correctly and completely Choices that have been made in the logical test design have been clearly indicated (for example, deviations from the technique) Appendix B 365 B.4 Physical test Description Y/N Not applicable Solution The test base on which the physical test case is based is clearly specified The physical test design is up to date, there are no major outstanding issues in the design specified in the test base The structure of the test design is clear The physical tests can be traced back to the related specification elements so changes in the test base can be efficiently implemented Physical tests can be traced back to the related risk category in the TRA so changes in the TRA can be efficiently implemented Physical tests can be traced back to the related logical test design so changes in the logical test design can efficiently implemented B.5 Test cases Description Y/N Not applicable Solution Test cases have a unique ID Test cases contain all the required fields Test cases are detailed, it is clear what the purpose of the test is and when the test is successful Test cases clearly describe which test actions will be executed Test cases describe the input data that is used and the expected outcome 366 Appendix B B.6 Test tools Description Y/N Not applicable Solution The test base on which the tooling is based is clearly indicated The tooling is up to date, there are no major outstanding issues in the design specified in the test base There are errors in the tooling and they have a clear status. It can be estimated which bug fixes/ changes will have to be performed on the tools Documentation for the tooling is available, so changes can be efficiently implemented The infrastructure is such that the tooling can be tested before the actual test run starts Appendix C – Checklist: Checklist smoke test system C.1 Conclusion Conclusion Y / N The system can enter the planned test phase. It is expected that the tests will run without too many obstacles or problems The required products are available and their content is sufficient The system is
Recommended publications
  • Data Sanity Check for Deep Learning Systems Via Learnt Assertions
    Data Sanity Check for Deep Learning Systems via Learnt Assertions Haochuan Lu∗y, Huanlin Xu∗y, Nana Liu∗y, Yangfan Zhou∗y, Xin Wang∗y ∗School of Computer Science, Fudan University, Shanghai, China yShanghai Key Laboratory of Intelligent Information Processing, Shanghai, China Abstract—Reliability is a critical consideration to DL- based deviations in a data flow perspective. Invalid input cases are systems. But the statistical nature of DL makes it quite vulnerable thus identified effectively. to invalid inputs, i.e., those cases that are not considered in We summarize the contributions of this paper as follows. the training phase of a DL model. This paper proposes to perform data sanity check to identify invalid inputs, so as to • We approach reliability enhancement of DL systems via enhance the reliability of DL-based systems. We design and data sanity check. We proposed a tool, namely SaneDL, implement a tool to detect behavior deviation of a DL model to perform data sanity check for DL-based systems. when processing an input case. This tool extracts the data flow SaneDL provides assertion mechanism to detect behavior footprints and conducts an assertion-based validation mechanism. The assertions are built automatically, which are specifically- deviation of DL model. To our knowledge, SaneDL is the tailored for DL model data flow analysis. Our experiments first assertion-based tool that can automatically detects conducted with real-world scenarios demonstrate that such an invalid input cases for DL systems. Our work can shed assertion-based data sanity check mechanism is effective in light to other practices in improving DL reliability.
    [Show full text]
  • Caradoc of the North Wind Free
    FREE CARADOC OF THE NORTH WIND PDF Allan Frewin Jones | 368 pages | 05 Apr 2012 | Hachette Children's Group | 9780340999417 | English | London, United Kingdom CARADOC OF THE NORTH WIND PDF As the war. Disaster strikes, and a valued friend suffers Caradoc of the North Wind devastating injury. Branwen sets off on a heroic journey towards destiny in an epic adventure of lovewar and revenge. Join Charlotte and Mia in this brilliant adventure full of princess sparkle and Christmas excitement! Chi ama i libri sceglie Kobo e inMondadori. The description is beautiful, but at times a bit too much, and sometimes at its worst the writing is hard to comprehend completely clearly. I find myself hoping vehemently for another book. It definitely allows the I read this in Caradoc of the North Wind sitting and could not put it down. Fair Wind to Widdershins. This author has published under several versions of his name, including Allan Jones, Frewin Jones, and A. Write a product review. Now he has stolen the feathers Caradoc of the North Wind Doodle, the weather-vane cockerel in charge of the weather. Jun 29, Katie rated it really liked it. Is the other warrior child, Arthur?? More than I thought I would, even. I really cafadoc want to know more, and off author is one that can really take you places. Join us by creating an account and start getting the best experience from our website! Jules Ember was raised hearing legends of wjnd ancient magic of the wicked Alchemist and the good Sorceress. Delivery and Returns see our delivery rates and policies thinking of returning an item? Mar 24, Valentina rated it really liked it.
    [Show full text]
  • Faithful Saliency Maps: Explaining Neural Networks by Augmenting "Competition for Pixels"
    Faithful Saliency Maps: Explaining Neural Networks by Augmenting "Competition for Pixels" The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Görns, Jorma Peer. 2020. Faithful Saliency Maps: Explaining Neural Networks by Augmenting "Competition for Pixels". Bachelor's thesis, Harvard College. Citable link https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364724 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Harvard University Senior Thesis Faithful Saliency Maps: Explaining Neural Networks by Augmenting "Competition for Pixels" Jorma Peer G¨orns Applied Mathematics supervised by Professor Himabindu Lakkaraju Cambridge, MA April 2020 Abstract For certain machine-learning models such as image classifiers, saliency methods promise to answer a crucial question: At the pixel level, where does the model look to classify a given image? If existing methods truthfully answer this question, they can bring some level of interpretability to an area of machine learning where it has been inexcusably absent: namely, to image-classifying neural networks, usually considered some of the most "black- box" classifiers. A multitude of different saliency methods has been developed over the last few years|recently, however, Adebayo et al. [1] revealed that many of them fail so- called "sanity checks": That is, these methods act as mere edge detectors of the input image, outputting the same convincing-looking saliency map completely independently of the model under investigation! Not only do they not illuminate the inner workings of the model at hand, but they may actually deceive the model investigator into believing that the model is working as it should.
    [Show full text]
  • Sys/Sys/Bio.H 1 1 /* 65 Void *Bio Caller2; /* Private Use by the Caller
    04/21/04 16:54:59 sys/sys/bio.h 1 1 /* 65 void *bio_caller2; /* Private use by the caller. */ 2 * Copyright (c) 1982, 1986, 1989, 1993 66 TAILQ_ENTRY(bio) bio_queue; /* Disksort queue. */ 3 * The Regents of the University of California. All rights reserved. 67 const char *bio_attribute; /* Attribute for BIO_[GS]ETATTR */ 4 * (c) UNIX System Laboratories, Inc. 68 struct g_consumer *bio_from; /* GEOM linkage */ 5 * All or some portions of this file are derived from material licensed 69 struct g_provider *bio_to; /* GEOM linkage */ 6 * to the University of California by American Telephone and Telegraph 70 off_t bio_length; /* Like bio_bcount */ 7 * Co. or Unix System Laboratories, Inc. and are reproduced herein with 71 off_t bio_completed; /* Inverse of bio_resid */ 8 * the permission of UNIX System Laboratories, Inc. 72 u_int bio_children; /* Number of spawned bios */ 9 * 73 u_int bio_inbed; /* Children safely home by now */ 10 * Redistribution and use in source and binary forms, with or without 74 struct bio *bio_parent; /* Pointer to parent */ 11 * modification, are permitted provided that the following conditions 75 struct bintime bio_t0; /* Time request started */ 12 * are met: 76 13 * 1. Redistributions of source code must retain the above copyright 77 bio_task_t *bio_task; /* Task_queue handler */ 14 * notice, this list of conditions and the following disclaimer. 78 void *bio_task_arg; /* Argument to above */ 15 * 2. Redistributions in binary form must reproduce the above copyright 79 16 * notice, this list of conditions and the following disclaimer in the 80 /* XXX: these go away when bio chaining is introduced */ 17 * documentation and/or other materials provided with the distribution. 81 daddr_t bio_pblkno; /* physical block number */ 18 * 3.
    [Show full text]
  • Hello, World! Free
    FREE HELLO, WORLD! PDF Disney Book Group | 14 pages | 16 Aug 2011 | Disney Press | 9781423141402 | English | New York, NY, United States "Hello, World!" program - Wikipedia Learn Data Science by completing interactive coding challenges and watching videos by expert instructors. Start Now! Python Hello a very simple language, and has a very straightforward syntax. It encourages programmers to program without boilerplate prepared code. The simplest directive in Python is the "print" directive - it simply prints out a line and also includes a newline, unlike in C. There Hello two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features. For example, World! difference between Python 2 and 3 is the print statement. In Hello 2, World! "print" statement is not a function, and therefore it is invoked without parentheses. However, in Python World!, it World! a function, and must be invoked with parentheses. Python uses indentation for World!, instead of curly braces. Both tabs and spaces are supported, but the standard indentation requires standard Python code to use four spaces. For example:. This site is generously supported by DataCamp. Join over a million other learners and get started learning Python for data science today! Hello, World! To print a string in Python 3, just write: print "This line will be printed. Hello Tutorial. Read our Terms of Use and Privacy Policy. Hello, World! - Learn Python - Free Interactive Python Tutorial A "Hello, World! Such a Hello is very simple in most programming World!and World! often used to illustrate the basic syntax of a programming language.
    [Show full text]
  • Bugdoc: Algorithms to Debug Computational Processes
    BugDoc: Algorithms to Debug Computational Processes Raoni Lourenço Juliana Freire Dennis Shasha New York University New York University New York University [email protected] [email protected] [email protected] Abstract Data analysis for scientific experiments and enterprises, large-scale simulations, and machine learning tasks all entail the use of complex computational pipelines to reach quanti- tative and qualitative conclusions. If some of the activities in a pipeline produce erroneous outputs, the pipeline may fail to execute or produce incorrect results. Inferring the root cause(s) of such failures is challenging, usually requiring time and much human thought, while still being error-prone. We propose a new approach that makes use of iteration and Figure 1: Machine learning pipeline and its prove- provenance to automatically infer the root causes and de- nance. A data scientist can explore different input rive succinct explanations of failures. Through a detailed datasets and classifier estimators to identify a suitable experimental evaluation, we assess the cost, precision, and solution for a classification problem. recall of our approach compared to the state of the art. Our Example: Exploring Supernovas. In an astronomy experiment, experimental data and processing software is available for some visualizations of supernovas presented unusual arti- use, reproducibility, and enhancement. facts that could have indicated a discovery. The experimental CCS Concepts analysis consisted of multiple pipelines run at different sites, • Information systems → Data provenance. including data collection at the telescope site, data processing at a high-performance computing facility, and data analysis 1 Introduction run on the physicist’s desktop. After spending substantial Computational pipelines are widely used in many do- time trying to verify the results, the physicists found that mains, from astrophysics and biology to enterprise analytics.
    [Show full text]
  • Opengl Performer™ Programmer's Guide
    OpenGL Performer™ Programmer’s Guide 007-1680-060 CONTRIBUTORS Written by George Eckel and Ken Jones Edited by Rick Thompson and Susan Wilkening Illustrated by Chrystie Danzer and Chris Wengelski Production by Adrian Daley and Karen Jacobson Engineering contributions by Angus Dorbie, Tom Flynn, Yair Kurzion, Radomir Mech, Alexandre Naaman, Marcin Romaszewicz, Allan Schaffer, and Jenny Zhao COPYRIGHT © 1997, 2000 Silicon Graphics, Inc. All rights reserved; provided portions may be copyright in third parties, as indicated elsewhere herein. No permission is granted to copy, distribute, or create derivative works from the contents of this electronic documentation in any manner, in whole or in part, without the prior written permission of Silicon Graphics, Inc. LIMITED RIGHTS LEGEND The electronic (software) version of this document was developed at private expense; if acquired under an agreement with the USA government or any contractor thereto, it is acquired as "commercial computer software" subject to the provisions of its applicable license agreement, as specified in (a) 48 CFR 12.212 of the FAR; or, if acquired for Department of Defense units, (b) 48 CFR 227-7202 of the DoD FAR Supplement; or sections succeeding thereto. Contractor/manufacturer is Silicon Graphics, Inc., 1600 Amphitheatre Pkwy 2E, Mountain View, CA 94043-1351. TRADEMARKS AND ATTRIBUTIONS Silicon Graphics,IRIS, IRIS Indigo, IRIX, ImageVision Library, Indigo, Indy, InfiniteReality, Onyx, OpenGL, are registered trademarks, SGI, and CASEVision, Crimson, Elan Graphics, IRIS Geometry Pipeline, IRIS GL, IRIS Graphics Library, IRIS InSight, IRIS Inventor, Indigo Elan, Indigo2, InfiniteReality2, OpenGL Performer, Personal IRIS, POWER Series, Performance Co-Pilot, RealityEngine, RealityEngine2, SGI logo, and Showcase are trademarks of Silicon Graphics, Inc.
    [Show full text]
  • CS107, Lecture 16 Wrap-Up / What’S Next?
    CS107, Lecture 16 Wrap-Up / What’s Next? This document is copyright (C) Stanford Computer Science and Nick Troccoli, licensed under Creative Commons Attribution 2.5 License. All rights reserved. Based on slides created by Marty Stepp, Cynthia Lee, Chris Gregg, and others. 1 Plan For Today • Recap: Where We’ve Been • Larger Applications • What’s Next? • Q&A 2 Plan For Today • Recap: Where We’ve Been • Larger Applications • What’s Next? • Q&A 3 We’ve covered a lot in just 10 weeks! Let’s take a look back. 4 Our CS107 Journey Arrays Bits and and Heap Bytes Pointers Generics Allocators C Strings Stack and Assembly Heap 5 Course Overview 1. Bits and Bytes - How can a computer represent integer numbers? 2. Chars and C-Strings - How can a computer represent and manipulate more complex data like text? 3. Pointers, Stack and Heap – How can we effectively manage all types of memory in our programs? 4. Generics - How can we use our knowledge of memory and data representation to write code that works with any data type? 5. Assembly - How does a computer interpret and execute C programs? 6. Heap Allocators - How do core memory-allocation operations like malloc and free work? 6 First Day /* * hello.c * This program prints a welcome message * to the user. */ #include <stdio.h> // for printf int main(int argc, char *argv[]) { printf("Hello, world!\n"); return 0; } 7 First Day • The command-line is a text-based interface to navigate a computer, instead of a Graphical User Interface (GUI). Graphical User Interface Text-based interface 8 Bits And Bytes Key Question: How can a computer represent integer numbers? 9 Bits And Bytes Why does this matter? • Limitations of representation and arithmetic impact programs! • We can also efficiently manipulate data using bits.
    [Show full text]
  • Extracting Detailed Tobacco Exposure from the Electronic Health Record
    Extracting Detailed Tobacco Exposure From The Electronic Health Record By Travis John Osterman Thesis Submitted to the Faculty of the Graduate School of Vanderbilt University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Biomedical Informatics August 11, 2017 Nashville, Tennessee Approved Josh Denny, M.D., M.S. Mia Levy, M.D., Ph.D. Pierre Massion, M. D. i To Laura, Owen and Gavin. Thank you for your patience, encouragement, and support. ii ACKNOWLEDGEMENTS I would like to thank the National Library of Medicine and the Conquer Cancer Foundation for supporting my biomedical informatics training and research (LM007450, R01- LM010685). My appreciation and thanks also go to the Department of Biomedical Informatics faculty and students for their support. Specifically, I thank Cindy Gadd and Rischelle Jenkins for creating and maintaining a rigorous and enlightening curriculum and to my master’s committee Josh Denny, Mia Levy, and Pierre Massion, for supporting my education and broader career development. In addition, I would like to thank Wei-Qi Wei, Dara Mize, Julie Wu, and Lisa Bastarche who directly contributed to this work. Finally, I would like to thank my parents, wife, and children for supporting me through this program. iii TABLE OF CONTENTS Page DEDICATION ................................................................................................................................ ii ACKNOWLEDGEMENTS ..........................................................................................................
    [Show full text]
  • IRIS Performer™ Programmer's Guide
    IRIS Performer™ Programmer’s Guide Document Number 007-1680-040 CONTRIBUTORS Written by George Eckel Edited by Steven Hiatt Illustrated by Dany Galgani Production by Derrald Vogt and Linda Rae Sande Engineering contributions by Sharon Clay, Brad Grantham, Don Hatch, Jim Helman, Michael Jones, Martin McDonald, John Rohlf, Allan Schaffer, Chris Tanner, Jenny Zhao, Yair Kurzion, and Tom McReynolds © Copyright 1995 -1997 Silicon Graphics, Inc.— All Rights Reserved This document contains proprietary and confidential information of Silicon Graphics, Inc. The contents of this document may not be disclosed to third parties, copied, or duplicated in any form, in whole or in part, without the prior written permission of Silicon Graphics, Inc. RESTRICTED RIGHTS LEGEND Use, duplication, or disclosure of the technical data contained in this document by the Government is subject to restrictions as set forth in subdivision (c) (1) (ii) of the Rights in Technical Data and Computer Software clause at DFARS 52.227-7013 and/or in similar or successor clauses in the FAR, or in the DOD or NASA FAR Supplement. Unpublished rights reserved under the Copyright Laws of the United States. Contractor/manufacturer is Silicon Graphics, Inc., 2011 N. Shoreline Blvd., Mountain View, CA 94039-7311. Indigo, Indy, IRIS, IRIS Indigo, ImageVision Library, Onyx, OpenGL, Silicon Graphics, and the Silicon Graphics logo are registered trademarks and Crimson, Elan Graphics, Geometry Pipeline, Indigo Elan, Indigo2, IRIS GL, IRIS Graphics Library, IRIS InSight, IRIS Inventor, IRIS Performer, IRIX, Personal IRIS, POWER Series, Performance Co-Pilot, RealityEngine, RealityEngine2, and Showcase are trademarks of Silicon Graphics, Inc. AutoCAD is a registered trademark of Autodesk, Inc.
    [Show full text]
  • Programming in Standard ML
    Programming in Standard ML (DRAFT:VERSION 1.2 OF 11.02.11.) Robert Harper Carnegie Mellon University Spring Semester, 2011 Copyright c 2011 by Robert Harper. All Rights Reserved. This work is licensed under the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/us/, or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Preface This book is an introduction to programming with the Standard ML pro- gramming language. It began life as a set of lecture notes for Computer Science 15–212: Principles of Programming, the second semester of the in- troductory sequence in the undergraduate computer science curriculum at Carnegie Mellon University. It has subsequently been used in many other courses at Carnegie Mellon, and at a number of universities around the world. It is intended to supersede my Introduction to Standard ML, which has been widely circulated over the last ten years. Standard ML is a formally defined programming language. The Defi- nition of Standard ML (Revised) is the formal definition of the language. It is supplemented by the Standard ML Basis Library, which defines a com- mon basis of types that are shared by all implementations of the language. Commentary on Standard ML discusses some of the decisions that went into the design of the first version of the language. There are several implementations of Standard ML available for a wide variety of hardware and software platforms. The best-known compilers are Standard ML of New Jersey, MLton, Moscow ML, MLKit, and PolyML.
    [Show full text]
  • Classification of Computer Programs in the Scratch Online Community Lena Abdalla
    Classification of Computer Programs in the Scratch Online Community by Lena Abdalla Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science 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 27, 2020 Certified by. Andrew Sliwinski Research Affiliate Thesis Supervisor Accepted by . Katrina LaCurts Chair, Master of Engineering Thesis Committee 2 Classification of Computer Programs in the Scratch Online Community by Lena Abdalla Submitted to the Department of Electrical Engineering and Computer Science on January 27, 2020, in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science Abstract Scratch is a graphical programming platform that empowers children to create com- puter programs and realize their ideas. Although the Scratch online community is filled with a variety of diverse projects, many of these projects also share similarities. For example, they tend to fall into certain categories, including games, animations, stories, and more. Throughout this thesis, I describe the application of Natural Language Processing (NLP) techniques to vectorize and classify Scratch projects by type. This effort included constructing a labeled dataset of 873 Scratch projects and their corresponding types, to be used for training a supervised classifier model. This dataset was constructed through a collective process of consensus-based annotation by experts. To realize the goal of classifying Scratch projects by type, I first train an unsupervised model of meaningful vector representations for Scratch blocks based on the composition of 500,000 projects.
    [Show full text]