Why-Oriented End-User Debugging of Naïve Bayes Text Classification

Machine learning techniques are increasingly used in intelligent assistants, that is, software targeted at and continuously adapting to assist end users with email, shopping, and other tasks. Examples include desktop SPAM filters, recommender systems, and handwriting recognition. Fixing such intelligent assistants when they learn incorrect behavior, however, has received only limited attention. To directly support end-user “debugging” of assistant behaviors learned via statistical machine learning, we present a Why-oriented approach which allows users to ask questions about how the assistant made its predictions, provides answers to these “why” questions, and allows users to interactively change these answers to debug the assistant's current and future predictions. To understand the strengths and weaknesses of this approach, we then conducted an exploratory study to investigate barriers that participants could encounter when debugging an intelligent assistant using our approach, and the information those participants requested to overcome these barriers. To help ensure the inclusiveness of our approach, we also explored how gender differences played a role in understanding barriers and information needs. We then used these results to consider opportunities for Why-oriented approaches to address user barriers and information needs

Improving App Inventor Debugging Support

MIT App Inventor 2 (AI2) is a visual environment where programs for Android mobile devices are composed of blocks resembling puzzle pieces. App Inventor lowers barriers for novices by providing visual guidance for understanding programs and by reducing common programming errors, but it does not eliminate errors entirely. Preliminary analysis of the users’ runtime errors shows that better debugging tools for AI2 are needed. People often encounter the same error or a series of errors before they find a solution or give up. I have implemented AI2 features to help programmers pinpoint the source of runtime errors. In live development mode, AI2 users can test on their devices blocks programs written in a web browser on their computer. Previously, often cryptic runtime error messages were displayed in a dialogue box in the browser window. With my changes, more meaningful runtime error messages are displayed on the block causing the error. I have also implemented a watch feature that allows users to track values of variables and expressions. My version is an improved version of the watch from App Inventor Classic. I have also implemented the means to collect more meaningful data on users errors. Currently, runtime error reports in live development mode are automatically stored in a cloud database. The reports include only the error message, the time of the error, and the device on which it was generated. I have augmented the error reports to include the current program code and its author. This extra information can be used to better understand who generates errors, why they are generated, and how users try to fix them. This information will support better debugging and, in the future, can be a basis for an intelligent debugging tutor

Visual Debugging of Object-Oriented Systems with the Unified Modeling Language

The Department of Defense (DoD) is developing a Joint Battlespace Infosphere, linking a large number of data sources and user applications. Debugging and analysis tools are required to aid in this process. Debugging of large object-oriented systems is a difficult cognitive process that requires understanding of both the overall and detailed behavior of the application. In addition, many such applications linked through a distributed system add to this complexity. Standard debuggers do not utilize visualization techniques, focusing mainly on information extracted directly from the source code. To overcome this deficiency, this research designs and implements a methodology that enables developers to analyze, troubleshoot and evaluate object-oriented systems using visualization techniques. It uses the standard UML class diagram coupled with visualization features such as focus+context, animation, graph layout, color encoding and filtering techniques to organize and present information in a manner that facilitates greater program and system comprehension. Multiple levels of abstraction, from low-level details such as source code and variable information to high-level structural detail in the form of a UML class diagram are accessible along with views of the program s control flow. The methods applied provide a considerable improvement (up to 1110%) in the number of classes that can be displayed in a set display area while still preserving user context and the semantics of UML, thus maintaining system understanding. Usability tests validated the application in terms of three criteria software visualization, debugging, and general system usability

Principio de inmediación y la audiencia penal virtual durante el Covid-19 en la Corte Superior de Lima Norte, 2021

El principio de inmediación implica el contacto directo entre el juez, la prueba y los órganos de prueba; en ese sentido, planteamos como objetivo general analizar de qué forma es transgredido el principio de inmediación en la audiencia penal virtual durante el Covid-19 en la Corte Superior de Lima Norte, 2021. En lo que respecta a la metodología del presente estudio, se tienen los métodos: hermenéutico, inductivo y descriptivo; enfoque cualitativo; tipo de investigación básica; diseño de teoría fundamentada; nivel descriptivo. Nuestro escenario de estudio fue en la Corte Superior de Lima Norte, Ministerio Público y estudios jurídicos aledaños; los participantes fueron: fiscales: provinciales y adjuntos, asistentes: en función fiscal y administrativo; y, abogados litigantes. Las técnicas empleadas fueron la entrevista y el análisis de documentos; los instrumentos fueron la guía de entrevista y la ficha de análisis documental. Por último, como resultado y conclusión se tiene que, el principio de inmediación sí es trasgredido en el desarrollo de las audiencias penales virtuales; ello a razón de la mala conectividad, falta de conocimiento sobre el uso de estos dispositivos electrónicos, ubicación geográfica de los sujetos procesales y las continuas fallas técnicas en el servicio de internet

DRAFT : Task System and Item Architecture (TSIA)

During its execution, a task is independent of all other tasks. For an application which executes in terms of tasks, the application definition can be free of the details of the execution. Many projects have demonstrated that a task system (TS) can provide such an application with a parallel, distributed, heterogeneous, adaptive, dynamic, real-time, interactive, reliable, secure or other execution. A task consists of items and thus the application is defined in terms of items. An item architecture (IA) can support arrays, routines and other structures of items, thus allowing for a structured application definition. Taking properties from many projects, the support can extend through to currying, application defined types, conditional items, streams and other definition elements. A task system and item architecture (TSIA) thus promises unprecedented levels of support for application execution and definition.Comment: vii+244 pages, including 126 figures of diagrams and code examples. Submitted to Springer Verlag. For further information see http://www.tsia.or

Web interface agent for debugging e-commerce

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2003.Includes bibliographical references (p. 146-150).Woodstein is a software agent that works with a user's web browser to explain and help diagnose problems in web processes, such as purchases. It enables the user to inspect data items in ordinary web pages, revealing the processes that created them. It provides an integrated view of the processes and data associated with a user's actions at a web site, and retrieves related information on the same web site, or even on different web sites. When the user inspects data that looks incorrect, Woodstein helps manage hypotheses about causally related data and processes that look incorrect and provides guidance in the process of elimination to isolate the unsuccessful process or wrong data.by Earl Joseph Wagner.S.M

Entwurf eingebetteter Systeme: Ausführbare Modelle und Fehlersuche

Graphische Modelle können mit Hilfe modellgetriebener Ansätze als Programmiersprachen für eingebettete Systeme genutzt werden. Die Arbeit schließt vorhandene Lücken und liefert einen Beitrag zur Zustandsvisualisierung während der Laufzeit zum Zweck der Fehlersuche in solchen Modellen. Dieser resultiert in der flexiblen Debugging-Architektur ModelScope. Die Leistungsfähigkeit von ModelScope wird durch Anbindung unterschiedlicher Modellierungsparadigmen und Ausführungsplattformen validiert