Exploiting Context in Dealing with Programming Errors and Exceptions in the IDE

Studies show that software developers spend about 19% of their development time in web surfing. While collecting necessary information using traditional web search, they face several practical challenges. First, it does not consider context (i.e., surroundings, circumstances) of the programming problems during search unless the developers do so in search query formulation, and forces the developers to frequently switch between their working environment (e.g., IDE) and the web browser. Second, technical details (e.g., stack trace) of an encountered exception often contain a lot of information, and they cannot be directly used as a search query given that the traditional search engines do not support long queries. Third, traditional search generally returns hundreds of search results, and the developers need to manually analyze the result pages one by one in order to extract a working solution. Both manual analysis of a page for content relevant to the encountered exception (and its context) and working an appropriate solution out are non-trivial tasks. Traditional code search engines share the same set of limitations of the web search ones, and they also do not help much in collecting the code examples that can be used for handling the encountered exceptions. In this thesis, we present a context-aware and IDE-based approach that helps one overcome those four challenges above. In our first study, we propose and evaluate a context-aware meta search engine for programming errors and exceptions. The meta search collects results for any encountered exception in the IDE from three popular search engines- Google, Bing and Yahoo and one programming Q & A site- StackOverflow, refines and ranks the results against the detailed context of the encountered exception, and then recommends them within the IDE. From this study, we not only explore the potential of the context-aware and meta search based approach but also realize the significance of appropriate search queries in searching for programming solutions. In the second study, we propose and evaluate an automated query recommendation approach that exploits the technical details of an encountered exception, and recommends a ranked list of search queries. We found the recommended queries quite promising and comparable to the queries suggested by experts. We also note that the support for the developers can be further complemented by post-search content analysis. In the third study, we propose and evaluate an IDE-based context-aware content recommendation approach that identifies and recommends sections of a web page that are relevant to the encountered exception in the IDE. The idea is to reduce the cognitive effort of the developers in searching for content of interest (i.e., relevance) in the page, and we found the approach quite effective through extensive experiments and a limited user study. In our fourth study, we propose and evaluate a context-aware code search engine that collects code examples from a number of code repositories of GitHub, and the examples contain high quality handlers for the exception of interest. We validate the performance of each of our proposed approaches against existing relevant literature and also through several mini user studies. Finally, in order to further validate the applicability of our approaches, we integrate them into an Eclipse plug in prototype--ExcClipse. We then conduct a task-oriented user study with six participants, and report the findings which are significantly promising

Retrieving curated Stack Overflow Posts of similar project tasks

Software development depends on diverse technologies and methods and as a result, software development teams often handle issues in which team members are not experts. In order to address this lack of expertise, developers typically search for information on web-based Q&A sites such as Stack Overflow, a well-known place to find solutions to specific technology-related problems. Access to these web-based Q&A locations is currently not integrated into the software development environment, and since the associations between software development projects and the supporting sources of known solutions, usually referred to as knowledge, is not explicitly recorded, software developers often need to search for solutions to similar recurring issues multiple times. This lack of integration hinders the reuse of the knowledge obtained, besides not avoiding efforts of search and selection, curation, of this knowledge over and over again. This research aims at proposing a study regarding explicitly associating project elements (such as project tasks) to Stack Overflow posts that have already been curated by developers, and presents a study about Stack Overflow posts suggestions to developers based on similarity of project tasks.O desenvolvimento de software depende de diversas tecnologias e métodos e, como resultado, as equipes de desenvolvimento de software geralmente lidam com problemas em que não são especialistas. Para lidar com a falta de conhecimento, desenvolvedores normalmente procuram informações em sites de perguntas e respostas, como o Stack Overflow, um site usado para encontrar soluções para problemas específicos relacionados à tecnologia. O acesso a esses sites não é integrado ao ambiente de desenvolvimento de software e porque as associações entre os projetos de desenvolvimento de software e as fontes de suporte de soluções conhecidas não são explicitamente registradas. Com isso, desenvolvedores de software podem investir um esforço em procurar soluções para problemas semelhantes várias vezes. Essa falta de integração dificulta o reuso do conhecimento obtido, além de não evitar esforços de busca e seleção, a curadoria, repetidas vezes. Esta pesquisa tem como objetivo realizar um estudo sobre a associação explicita entre elementos do projeto (como tarefas de projeto) a publicações do Stack Overflow que já sofreram curadoria por desenvolvedores, e apresenta um estudo sobre sugestões de publicações do Stack Overflow a desenvolvedores com base na similaridade de tarefas de projeto

Supporting Source Code Search with Context-Aware and Semantics-Driven Query Reformulation

Software bugs and failures cost trillions of dollars every year, and could even lead to deadly accidents (e.g., Therac-25 accident). During maintenance, software developers fix numerous bugs and implement hundreds of new features by making necessary changes to the existing software code. Once an issue report (e.g., bug report, change request) is assigned to a developer, she chooses a few important keywords from the report as a search query, and then attempts to find out the exact locations in the software code that need to be either repaired or enhanced. As a part of this maintenance, developers also often select ad hoc queries on the fly, and attempt to locate the reusable code from the Internet that could assist them either in bug fixing or in feature implementation. Unfortunately, even the experienced developers often fail to construct the right search queries. Even if the developers come up with a few ad hoc queries, most of them require frequent modifications which cost significant development time and efforts. Thus, construction of an appropriate query for localizing the software bugs, programming concepts or even the reusable code is a major challenge. In this thesis, we overcome this query construction challenge with six studies, and develop a novel, effective code search solution (BugDoctor) that assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, we reformulate a given search query (1) by designing novel keyword selection algorithms (e.g., CodeRank) that outperform the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. Our experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that our proposed approach can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that our approach can outperform the state-of-the-art approaches with a significant margin