NLP2Code: Code Snippet Content Assist via Natural Language Tasks

Developers increasingly take to the Internet for code snippets to integrate into their programs. To save developers the time required to switch from their development environments to a web browser in the quest for a suitable code snippet, we introduce NLP2Code, a content assist for code snippets. Unlike related tools, NLP2Code integrates directly into the source code editor and provides developers with a content assist feature to close the vocabulary gap between developers' needs and code snippet meta data. Our preliminary evaluation of NLP2Code shows that the majority of invocations lead to code snippets rated as helpful by users and that the tool is able to support a wide range of tasks.Comment: tool demo video available at https://www.youtube.com/watch?v=h-gaVYtCznI; to appear as a tool demo paper at ICSME 2017 (https://icsme2017.github.io/

Mining and linking crowd-based software engineering how-to screencasts

In recent years, crowd-based content in the form of screencast videos has gained in popularity among software engineers. Screencasts are viewed and created for different purposes, such as a learning aid, being part of a software project’s documentation, or as a general knowledge sharing resource. For organizations to remain competitive in attracting and retaining their workforce, they must adapt to these technological and social changes in software engineering practices. In this thesis, we propose a novel methodology for mining and integrating crowd-based multi- media content in existing workflows to help provide software engineers of different levels of experience and roles access to a documentation they are familiar with or prefer. As a result, we first aim to gain insights on how a user’s background and the task to be performed influence the use of certain documentation media. We focus on tutorial screencasts to identify their important information sources and provide insights on their usage, advantages, and disadvantages from a practitioner’s perspective. To that end, we conduct a survey of software engineers. We discuss how software engineers benefit from screencasts as well as challenges they face in using screencasts as project documentation. Our survey results revealed that screencasts and question and answers sites are among the most popular crowd-based information sources used by software engineers. Also, the level of experience and the role or reason for resorting to a documentation source affects the types of documentation used by software engineers. The results of our survey support our motivation in this thesis and show that for screencasts, high quality content and a narrator are very important components for users. Unfortunately, the binary format of videos makes analyzing video content difficult. As a result, dissecting and filtering multimedia information based on its relevance to a given project is an inherently difficult task. Therefore, it is necessary to provide automated approaches for mining and linking this crowd-based multimedia documentation to their relevant software artifacts. In this thesis, we apply LDA-based (Latent Dirichlet Allocation) mining approaches that take as input a set of screencast artifacts, such as GUI (Graphical User Interface) text (labels) and spoken words, to perform information extraction and, therefore, increase the availability of both textual and multimedia documentation for various stakeholders of a software product. For example, this allows screencasts to be linked to other software artifacts such as source code to help software developers/maintainers have access to the implementation details of an application feature. We also present applications of our proposed methodology that include: 1) an LDA-based mining approach that extracts use case scenarios in text format from screencasts, 2) an LDA-based approach that links screencasts to their relevant artifacts (e.g., source code), and 3) a Semantic Web-based approach to establish direct links between vulnerability exploitation screencasts and their relevant vulnerability descriptions in the National Vulnerability Database (NVD) and indirectly link screencasts to their relevant Maven dependencies. To evaluate the applicability of the proposed approach, we report on empirical case studies conducted on existing screencasts that describe different use case scenarios of the WordPress and Firefox open source applications or vulnerability exploitation scenarios

Human–Computer Interaction and Participation in Software Crowdsourcing

Improvements in communication and networking technologies have transformed people’s lives and organizations’ activities. Web 2.0 innovation has provided a variety of hybridized applications and tools that have changed enterprises’ functional and communication processes. People use numerous platforms to broaden their social contacts, select items, execute duties, and learn new things. Context: Crowdsourcing is an internet-enabled problem-solving strategy that utilizes human–computer interaction to leverage the expertise of people to achieve business goals. In crowdsourcing approaches, three main entities work in collaboration to solve various problems. These entities are requestors (job providers), platforms, and online users. Tasks are announced by requestors on crowdsourcing platforms, and online users, after passing initial screening, are allowed to work on these tasks. Crowds participate to achieve various rewards. Motivation: Crowdsourcing is gaining importance as an alternate outsourcing approach in the software engineering industry. Crowdsourcing application development involves complicated tasks that vary considerably from the micro-tasks available on platforms such as Amazon Mechanical Turk. To obtain the tangible opportunities of crowdsourcing in the realm of software development, corporations should first grasp how this technique works, what problems occur, and what factors might influence community involvement and co-creation. Online communities have become more popular recently with the rise in crowdsourcing platforms. These communities concentrate on specific problems and help people with solving and managing these problems. Objectives: We set three main goals to research crowd interaction: (1) find the appropriate characteristics of social crowd utilized for effective software crowdsourcing, (2) highlight the motivation of a crowd for virtual tasks, and (3) evaluate primary participation reasons by assessing various crowds using Fuzzy AHP and TOPSIS method. Conclusion: We developed a decision support system to examine the appropriate reasons of crowd participation in crowdsourcing. Rewards and employments were evaluated as the primary motives of crowds for accomplishing tasks on crowdsourcing platforms, knowledge sharing was evaluated as the third reason, ranking was the fourth, competency was the fifth, socialization was sixth, and source of inspiration was the seventh.Princess Nourah bint Abdulrahman University Researchers Supporting - Riyadh, Saudi Arabia. Project number (PNURSP2023TR140)

Facilitating crowd sourced software engineering via stack overflow

The open source community, as well as numerous technical blogs and community web sites, put online vast quantities of free source code, ranging from snippets to full-blown products. This code embodies the software development community’s domain knowledge, and mirrors the structure of the Internet: it is distributed rather than hierarchical; it is chaotic, incomplete, and inconsistent. Stack- Overflow.com is a Question and Answer (Q&A) website which uses social media to facilitate knowledge exchange between programmers by mitigating the pitfalls involved in using code from the Internet. Its design nurtures a community of developers, and enables crowd sourced software engineering activities ranging from documentation to providing useful, high quality code snippets to be used in production. In this chapter we review Stack Overflow from three perspectives: (1) its design and its social media characteristics, (2) the role it plays in the software documentation landscape, and (3) the use of Stack Overflow in the context of the example centric programming paradigm.Ohad Barzilay, Christoph Treude, and Alexey Zagalsk

CodeSpace

Orientador : Prof. Alexander Robert KutzkeMonografia (graduação) - Universidade Federal do Paraná, Setor de Educação Profissional e Tecnológica, Curso Superior de Tecnologia em Análise e Desenvolvimento de SistemasInclui referênciasResumo : O CodeSpace é uma plataforma virtual destinada a fornecer ao público de língua portuguesa conceitos importantes e códigos completos de programas computacionais de inúmeras linguagens. Além de atender à essa antiga demanda, já que muitos usuários desse tipo de serviço não dominam o inglês, o CodeSpace oferece um conjunto de características que facilitam sua utilização e inspiram o desenvolvimento de uma comunidade comprometida com a difusão dessa área de conhecimento, que é cada vez mais valorizada no mercado. O sistema possui uma interface amigável, um leiaute moderno e responsivo e uma estrutura de navegação intuitiva. O usuário, além de buscar artigos e se comunicar com seus autores, também é inspirado a produzir seu próprio conteúdo, mesmo que seja novato no mundo da programação. Em seu modelo de funcionamento, a própria comunidade cria e compartilha o conteúdo do website, contando, também, com a opção de votar positivamente ou negativamente em cada publicação do portal. A plataforma desenvolvida neste trabalho aparece no horizonte de serviços similares de forma promissora, valorizando o público lusófono, atraindo usuários menos experientes e inspirando, acima de tudo, o compartilhamento de ideias

Beyond Traditional Software Development: Studying and Supporting the Role of Reusing Crowdsourced Knowledge in Software Development

As software development is becoming increasingly complex, developers often need to reuse others’ code or knowledge made available online to tackle problems encountered during software development and maintenance. This phenomenon of using others' code or knowledge, often found on online forums, is referred to as crowdsourcing. A good example of crowdsourcing is posting a coding question on the Stack Overflow website and having others contribute code that solves that question. Recently, the phenomenon of crowdsourcing has attracted much attention from researchers and practitioners and recent studies show that crowdsourcing improves productivity and reduces time-to-market. However, like any solution, crowdsourcing brings with it challenges such as quality, maintenance, and even legal issues. The research presented in this thesis presents the result of a series of large-scale empirical studies involving some of the most popular crowdsourcing platforms such as Stack Overflow, Node Package Manager (npm), and Python Package Index (PyPI). The focus of these empirical studies is to investigate the role of reusing crowdsourcing knowledge and more particularly crowd code in the software development process. We first present two empirical studies on the reuse of knowledge from crowdsourcing platforms namely Stack Overflow. We found that reusing knowledge from this crowdsourcing platform has the potential to assist software development practices, specifically through source code reuse. However, relying on such crowdsourced knowledge might also negatively affect the quality of the software projects. Second, we empirically examine the type of development knowledge constructed on crowdsourcing platforms. We examine the use of trivial packages on npm and PyPI platforms. We found that trivial packages are common and developers tend to use them because they provide them with well tested and implemented code. However, developers are concerned about the maintenance overhead of these trivial packages due to the extra dependencies that trivial packages introduce. Finally, we used the gained knowledge to propose a pragmatic solution to improve the efficiency of relying on the crowd in software development. We proposed a rule-based technique that automatically detects commits that can skip the continuous integration process. We evaluate the performance of the proposed technique on a dataset of open-source Java projects. Our results show that continuous integration can be used to improve the efficiency of the reused code from crowdsourcing platforms. Among the findings of this thesis are that the way software is developed has changed dramatically. Developers rely on crowdsourcing to address problems encountered during software development and maintenance. The results presented in this thesis provides new insights on how knowledge from these crowdsourced platforms is reused in software systems and how some of this knowledge can be better integrated into current software development processes and best practices

Multi-objective Search-based Mobile Testing

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis. Since crowdsourcing is, itself, an emerging research area, and less well understood than search-based software engineering, the thesis also provides the first comprehensive survey on the use of crowdsourcing in software testing (in particular) and in software engineering (more generally). This survey represents a secondary contribution. Sapienz is an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising their length, while simultaneously maximising their coverage and fault revelation. The results of empirical studies demonstrate that Sapienz significantly outperforms both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, on all three objectives. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. Octopuz reuses the Sapienz multi-objective search approach for automated JavaScript testing, aiming to investigate whether it replicates the Sapienz’ success on JavaScript testing. Experimental results on 10 real-world JavaScript apps provide evidence that Octopuz significantly outperforms the state of the art (and current state of practice) in automated JavaScript testing. Polariz is an approach that combines human (crowd) intelligence with machine (computational search) intelligence for mobile testing. It uses a platform that enables crowdsourced mobile testing from any source of app, via any terminal client, and by any crowd of workers. It generates replicable test scripts based on manual test traces produced by the crowd workforce, and automatically extracts from these test traces, motif events that can be used to improve search-based mobile testing approaches such as Sapienz