Gender : an important factor in end-user programming environments?

A human-centric issue that has not been considered in the design of end-user programming environments is whether gender differences exist that are important to the design of these environments. Ignoring this issue would miss the opportunity of enhancing the effectiveness of end-user programmers by incorporating appropriate mechanisms to support gender-associated differences in decision making, learning, and problem solving. This paper takes a first step toward building a foundation for investigating this issue by surveying gender difference literature from five domains from the perspective of possible implications for end-user programming. We present a taxonomy of this literature, and derive a number of specific issues for each element of the taxonomy (stated as hypotheses). This foundation provides a starting point for organized investigations into issues that may be important for making breakthroughs in the effectiveness of end-user programmers

Too much, too little, or just right? Ways explanations impact end users' mental models

Research is emerging on how end users can correct mistakes their intelligent agents make, but before users can correctly "debug" an intelligent agent, they need some degree of understanding of how it works. In this paper we consider ways intelligent agents should explain themselves to end users, especially focusing on how the soundness and completeness of the explanations impacts the fidelity of end users' mental models. Our findings suggest that completeness is more important than soundness: increasing completeness via certain information types helped participants' mental models and, surprisingly, their perception of the cost/benefit tradeoff of attending to the explanations. We also found that oversimplification, as per many commercial agents, can be a problem: when soundness was very low, participants experienced more mental demand and lost trust in the explanations, thereby reducing the likelihood that users will pay attention to such explanations at all

Monitoring interface and automated testing for Seaweed, a web-based economic game system

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 47-49).Seaweed is a web-based economic game system that allows end users to design and deploy simple two-player economic games. To improve the usability of the system, we have created two new features. One: a monitoring interface that allows users to view the results of a game deployment in real time. Two: an automated testing feature that automatically generates play data for a game, so that the user can check that the results are correct. Challenges included condensing the data of multiple plays of a game into a readable format and modifying the computer player system to pit two computer players against each other in order to generate the test data.by Jessica Yuan.M.Eng

FuzzTheREST - Intelligent Automated Blackbox RESTful API Fuzzer

In recent years, the pervasive influence of technology has deeply intertwined with human life, impacting diverse fields. This relationship has evolved into a dependency, with software systems playing a pivotal role, necessitating a high level of trust. Today, a substantial portion of software is accessed through Application Programming Interfaces, particularly web APIs, which predominantly adhere to the Representational State Transfer architecture. However, this architectural choice introduces a wide range of potential vulnerabilities, which are available and accessible at a network level. The significance of Software testing becomes evident when considering the widespread use of software in various daily tasks that impact personal safety and security, making the identification and assessment of faulty software of paramount importance. In this thesis, FuzzTheREST, a black-box RESTful API fuzzy testing framework, is introduced with the primary aim of addressing the challenges associated with understanding the context of each system under test and conducting comprehensive automated testing using diverse inputs. Operating from a black-box perspective, this fuzzer leverages Reinforcement Learning to efficiently uncover vulnerabilities in RESTful APIs by optimizing input values and combinations, relying on mutation methods for input exploration. The system's value is further enhanced through the provision of a thoroughly documented vulnerability discovery process for the user. This proposal stands out for its emphasis on explainability and the application of RL to learn the context of each API, thus eliminating the necessity for source code knowledge and expediting the testing process. The developed solution adheres rigorously to software engineering best practices and incorporates a novel Reinforcement Learning algorithm, comprising a customized environment for API Fuzzy Testing and a Multi-table Q-Learning Agent. The quality and applicability of the tool developed are also assessed, relying on the results achieved on two case studies, involving the Petstore API and an Emotion Detection module which was part of the CyberFactory#1 European research project. The results demonstrate the tool's effectiveness in discovering vulnerabilities, having found 7 different vulnerabilities and the agents' ability to learn different API contexts relying on API responses while maintaining reasonable code coverage levels.Ultimamente, a influência da tecnologia espalhou-se pela vida humana de uma forma abrangente, afetando uma grande diversidade dos seus aspetos. Com a evolução tecnológica esta acabou por se tornar uma dependência. Os sistemas de software começam assim a desempenhar um papel crucial, o que em contrapartida obriga a um elevado grau de confiança. Atualmente, uma parte substancial do software é implementada em formato de Web APIs, que na sua maioria seguem a arquitetura de transferência de estado representacional. No entanto, esta introduz uma série vulnerabilidade. A importância dos testes de software torna-se evidente quando consideramos o amplo uso de software em várias tarefas diárias que afetam a segurança, elevando ainda mais a importância da identificação e mitigação de falhas de software. Nesta tese é apresentado o FuzzTheREST, uma framework de teste fuzzy de APIs RESTful num modelo caixa preta, com o objetivo principal de abordar os desafios relacionados com a compreensão do contexto de cada sistema sob teste e a realização de testes automatizados usando uma variedade de possíveis valores. Este fuzzer utiliza aprendizagem por reforço de forma a compreender o contexto da API que está sob teste de forma a guiar a geração de valores de teste, recorrendo a métodos de mutação, para descobrir vulnerabilidades nas mesmas. Todo o processo desempenhado pelo sistema é devidamente documentado para que o utilizador possa tomar ações mediante os resultados obtidos. Esta explicabilidade e aplicação de inteligência artificial para aprender o contexto de cada API, eliminando a necessidade de analisar código fonte e acelerando o processo de testagem, enaltece e distingue a solução proposta de outras. A solução desenvolvida adere estritamente às melhores práticas de engenharia de software e inclui um novo algoritmo de aprendizagem por reforço, que compreende um ambiente personalizado para testagem Fuzzy de APIs e um Agente de QLearning com múltiplas Q-tables. A qualidade e aplicabilidade da ferramenta desenvolvida também são avaliadas com base nos resultados obtidos em dois casos de estudo, que envolvem a conhecida API Petstore e um módulo de Deteção de Emoções que fez parte do projeto de investigação europeu CyberFactory#1. Os resultados demonstram a eficácia da ferramenta na descoberta de vulnerabilidades, tendo identificado 7 vulnerabilidades distintas, e a capacidade dos agentes em aprender diferentes contextos de API com base nas respostas da mesma, mantendo níveis de cobertura aceitáveis

Design of Back-End of Recommendation Systems Using Collective Intelligence Social Tagging

Recommendation systems are the tools whose purpose is to suggest relevant products or services to the customers. In a movie business website, the recommendation system provides users with more options, classify movies under different types to assist in arriving at a decision. Although, with current e-commerce giants focusing on hybrid filtering approach, we have decided to explore the functionality of Content-based recommendation system. This research paper aims to delve deeper into the content-based recommendation system and adding tags to enhance its functionality. The content-based approach is more fit to the movie recommendation as it overcomes the ‘cold start’ issue faced by the collaborative filtering approach, meaning, even with no ratings for a movie, it can still be recommended. The proposed method is to solve the less ‘data categorization’ issue in content-based filtering. Collective Intelligence Social Tagging System (CIST) aims at making a significant difference in content-based recommendation system to enrich the item profile and provide more accurate suggestions. The main gist of CIST is to involve the users to contribute in tagging to build a more robust system in online movie businesses. Tags in the millennial world are the ‘go to’ words that everyone looks up to in an online world of E-commerce. It’s the easiest way of telling a story without actual long sentences. We recommended three main solutions for the concerns of CIST, (a) clustering of tags to avoid synonymous tag confusion and create a metadata for movies under same tags, (b) 5 criteria model to motivate and give the most amount of genuine information for end users to trust and eventually contribute in tagging, and (c) clear way of distinguishing and displaying tags to separate primary tags and secondary tags and give a chance to the users to assess whether the given tags reflect the relevant theme of the film

Supporting end-user debugging : what do users want to know?

Although researchers have begun to explicitly support end-user programmers' debugging by providing information to help them find bugs, there is little research addressing the right content to communicate to these users. The specific semantic content of these debugging communications matters because, if the users are not actually seeking the information the system is providing, they are not likely to attend to it. This paper reports a formative empirical study that sheds light on what end users actually want to know in the course of debugging a spreadsheet, given the avail-ability of a set of interactive visual testing and debugging features. Our results provide insights into end-user debuggers' in-formation gaps, and further suggest opportunities to improve end-user debugging systems' support for the things end-user debuggers actually want to know.Keywords: end-user programming, online help, end-user development, end-user debugging, User Interfaces, Office Automation—Spreadsheets, User/Machine Systems—Software psychology, Testing and Debugging-Debugging Aids, Programming Environments-Interactive environments, End-user software engineering, Visual programmin

An empirical study of fault localization for end-user programmers

End users develop more software than any other group of programmers, using software authoring devices such as e-mail filtering editors, by-demonstration macro builders, and spreadsheet environments. Despite this, there has been little research on finding ways to help these programmers with the dependability of their software. We have been addressing this problem in several ways, one of which includes supporting end-user debugging activities through fault localization techniques. This paper presents the results of an empirical study conducted in an end-user programming environment to examine the impact of two separate factors in fault localization techniques that affect technique effectiveness. Our results shed new insights into fault localization techniques for end-user programmers and the factors that affect them, with significant implications for the evaluation of those techniques