A Systematic Review of Automated Query Reformulations in Source Code Search

Fixing software bugs and adding new features are two of the major maintenance tasks. Software bugs and features are reported as change requests. Developers consult these requests and often choose a few keywords from them as an ad hoc query. Then they execute the query with a search engine to find the exact locations within software code that need to be changed. Unfortunately, even experienced developers often fail to choose appropriate queries, which leads to costly trials and errors during a code search. Over the years, many studies attempt to reformulate the ad hoc queries from developers to support them. In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis (e.g., Grounded Theory), and then answer seven research questions with major findings. First, to date, eight major methodologies (e.g., term weighting, term co-occurrence analysis, thesaurus lookup) have been adopted to reformulate queries. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, vocabulary mismatch problem, subjective bias) that might prevent their wide adoption. Finally, we discuss the best practices and future opportunities to advance the state of research in search query reformulations.Comment: 81 pages, accepted at TOSE

The exploration of a category theory-based virtual Geometrical product specification system for design and manufacturing

In order to ensure quality of products and to facilitate global outsourcing, almost all the so-called “world-class” manufacturing companies nowadays are applying various tools and methods to maintain the consistency of a product’s characteristics throughout its manufacturing life cycle. Among these, for ensuring the consistency of the geometric characteristics, a tolerancing language − the Geometrical Product Specification (GPS) has been widely adopted to precisely transform the functional requirements from customers into manufactured workpieces expressed as tolerance notes in technical drawings. Although commonly acknowledged by industrial users as one of the most successful efforts in integrating existing manufacturing life-cycle standards, current GPS implementations and software packages suffer from several drawbacks in their practical use, possibly the most significant, the difficulties in inferring the data for the “best” solutions. The problem stemmed from the foundation of data structures and knowledge-based system design. This indicates that there need to be a “new” software system to facilitate GPS applications. The presented thesis introduced an innovative knowledge-based system − the VirtualGPS − that provides an integrated GPS knowledge platform based on a stable and efficient database structure with knowledge generation and accessing facilities. The system focuses on solving the intrinsic product design and production problems by acting as a virtual domain expert through translating GPS standards and rules into the forms of computerized expert advices and warnings. Furthermore, this system can be used as a training tool for young and new engineers to understand the huge amount of GPS standards in a relative “quicker” manner. The thesis started with a detailed discussion of the proposed categorical modelling mechanism, which has been devised based on the Category Theory. It provided a unified mechanism for knowledge acquisition and representation, knowledge-based system design, and database schema modelling. As a core part for assessing this knowledge-based system, the implementation of the categorical Database Management System (DBMS) is also presented in this thesis. The focus then moved on to demonstrate the design and implementation of the proposed VirtualGPS system. The tests and evaluations of this system were illustrated in Chapter 6. Finally, the thesis summarized the contributions to knowledge in Chapter 7. After thoroughly reviewing the project, the conclusions reached construe that the III entire VirtualGPS system was designed and implemented to conform to Category Theory and object-oriented programming rules. The initial tests and performance analyses show that the system facilitates the geometric product manufacturing operations and benefits the manufacturers and engineers alike from function designs, to a manufacturing and verification

Cognitive Ontology based Framework for Networking Women in Sciences

In order to increase the percentage of women in academics or researchers, there is need for a functioning research networking through which women can exchange ideas; ask questions and more importantly, mentorship, in Nigeria. In order to make for this, several recommendations have been suggested but are not scientific. Therefore, to bridge this gap scientifically, this paper is presenting an overview of a question and answering system framework that hybridize semantic search methodology and cognitive reasoning. The hybridization will enhance the question and answering accuracy especially due to the introduction of domain ontology for the semantic process. This paper also presents the output of the first phase of the implementation which is the development of the domain ontology for the question and answering syste

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

Managing corporate memory on the semantic web

Corporate memory (CM) is the total body of data, information and knowledge required to deliver the strategic aims and objectives of an organization. In the current market, the rapidly increasing volume of unstructured documents in the enterprises has brought the challenge of building an autonomic framework to acquire, represent, learn and maintain CM, and efficiently reason from it to aid in knowledge discovery and reuse. The concept of semantic web is being introduced in the enterprises to structure information in a machine readable way and enhance the understandability of the disparate information. Due to the continual popularity of the semantic web, this paper develops a framework for CM management on the semantic web. The proposed approach gleans information from the documents, converts into a semantic web resource using resource description framework (RDF) and RDF Schema and then identifies relations among them using latent semantic analysis technique. The efficacy of the proposed approach is demonstrated through empirical experiments conducted on two case studies. © 2014 Springer Science+Business Media New York

Incorporating WordNet in an Information Retrieval System

Query expansion is a method of modifying an initial query to enhance retrieval performance in information retrieval operations [11] . There are alternate ways to expand a user input query such as finding synonyms of words, re-weighting the query, fixing spelling mistakes, etc. [11] . In this project, we created a query rewriting algorithm, which uses synonyms for a given word for query expansion. These synonyms were chosen using WordNet, a lexical database for English [16] [15] . Similarity ranking functions and a part-of- speech tagger were written to extract the essential data from WordNet output. Various experiments were carried out after integrating WordNet in Yioop to evaluate the improvement in search results and its throughput