Same query - different results? A study of repeat queries in search sessions

Typically, three main query reformulation types in sessions are considered: generalization, specication, and drift. We show that given the full context of user interactions, repeat queries represent an important reformulation type which should also be addressed in session retrieval evaluation. We investigate dierent query reformulation patterns in logs from The European Library. Using an automatic classification for query reformulations, we found that the most frequent (and presumably the most important) reformulation pattern corresponds to repeat queries. We aim to nd possible explanations for repeat queries in sessions and try to uncover implications for session retrieval evaluation

Finding Structured and Unstructured Features to Improve the Search Result of Complex Question

-Recently, search engine got challenge deal with such a natural language questions. Sometimes, these questions are complex questions. A complex question is a question that consists several clauses, several intentions or need long answer. In this work we proposed that finding structured features and unstructured features of questions and using structured data and unstructured data could improve the search result of complex questions. According to those, we will use two approaches, IR approach and structured retrieval, QA template. Our framework consists of three parts. Question analysis, Resource Discovery and Analysis The Relevant Answer. In Question Analysis we used a few assumptions, and tried to find structured and unstructured features of the questions. Structured feature refers to Structured data and unstructured feature refers to unstructured data. In the resource discovery we integrated structured data (relational database) and unstructured data (webpage) to take the advantaged of two kinds of data to improve and reach the relevant answer. We will find the best top fragments from context of the webpage In the Relevant Answer part, we made a score matching between the result from structured data and unstructured data, then finally used QA template to reformulate the question. In the experiment result, it shows that using structured feature and unstructured feature and using both structured and unstructured data, using approach IR and QA template could improve the search result of complex questions

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