scholarly journals Configuring and Assembling Information Retrieval Based Solutions for Software Engineering Tasks

2016 ◽  
Author(s):  
Bogdan Dit
Keyword(s):  
Information Retrieval ◽  
Software Engineering

Deep Learning

2020 ◽  
pp. 124-141 ◽  
Author(s):  
Menaga D. ◽  
Revathi S.
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Software Engineering ◽  
Multimedia Information ◽  
Research Area ◽  
Multimedia Application ◽  
Information Process ◽  
Display Devices ◽  
Storage Devices ◽  
Major Application

Multimedia application is a significant and growing research area because of the advances in technology of software engineering, storage devices, networks, and display devices. With the intention of satisfying multimedia information desires of users, it is essential to build an efficient multimedia information process, access, and analysis applications, which maintain various tasks, like retrieval, recommendation, search, classification, and clustering. Deep learning is an emerging technique in the sphere of multimedia information process, which solves both the crisis of conventional and recent researches. The main aim is to resolve the multimedia-related problems by the use of deep learning. The deep learning revolution is discussed with the depiction and feature. Finally, the major application also explained with respect to different fields. This chapter analyzes the crisis of retrieval after providing the successful discussion of multimedia information retrieval that is the ability of retrieving an object of every multimedia.

Automatically Labelled Software Topic Model

2020 ◽  
Vol 11 (1) ◽  
pp. 57-78
Author(s):  
Youcef Bouziane ◽  
Mustapha Kamel Abdi ◽  
Salah Sadou
Keyword(s):  
Information Retrieval ◽  
Software Engineering ◽  
Topic Model ◽  
Mining Software Repositories ◽  
Valuable Data ◽  
Empirical Results ◽  
Software Repositories ◽  
Manual Analysis ◽  
Novel Approach ◽  
Support Software

Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques. However, this technique does not give an interpretation nor labels to the extracted topics and it requires manual analysis to identify them. Some approaches were proposed to automatically label the topics using tags in SR, but they do not consider the existence of spam-tags and they have difficulties to scale to large tag space. This article introduces a novel approach called automatically labelled software topic model (AL-STM) that labels the topics based on observed tags in SR. It mitigates the shortcomings of manual and automatic labelling of topics in SE. AL-STM is implemented using 22K GitHub projects and evaluated in a SE task (tag recommending) against the currently used techniques. The empirical results suggest that AL-STM is more robust in terms of MAP and nDCG, and more scalable to large tag space.

Contextual Search: From Information Behaviour to Information Retrieval

2013 ◽  
Author(s):  
Luanne Freund ◽  
Elaine G. Toms
Keyword(s):  
Information Retrieval ◽  
Software Engineering ◽  
Information Seeking ◽  
Contextual Information ◽  
Search System ◽  
Information Behaviour ◽  
Information Seeking Behaviour ◽  
Engineering Work ◽  
Search Systems

Context influences information seeking behaviour; however, search systems have not made much use of contextual information to date. We present research that combines information behaviour and information retrieval approaches to develop a contextual search system for a software engineering work domain.Le contexte influence le comportement informationnel; cependant, les systèmes de recherche n'ont pas fait beaucoup d'utilisation de l'information contextuelle jusqu'ici. Nous présentons une étude qui combine des approches de comportement informationnel et de repérage d’information a fin de développer un système contextuel de recherche pour un domaine de travail de technologie de la programmation. 

scholarly journals Stopping duplicate bug reports before they start with Continuous Querying for bug reports

2016 ◽  
Author(s):  
Abram Hindle
Keyword(s):  
Information Retrieval ◽  
Software Engineering ◽  
Search Engines ◽  
Indexing Methods ◽  
Bug Reports ◽  
Bug Report ◽  
Engineering Information ◽  
String Search ◽  
The Creation ◽  
Duplicate Bug Reports

Bug deduplication is a hot topic in software engineering information retrieval research, but it is often not deployed. Typically to de-duplicate bug reports developers rely upon the search capabilities of the bug report software they employ, such as Bugzilla, Jira, or Github Issues. These search capabilities range from simple SQL string search to IR-based word indexing methods employed by search engines. Yet too often these searches do very little to stop the creation of duplicate bug reports. Some bug trackers have more than 10\% of their bug reports marked as duplicate. Perhaps these bug tracker search engines are not enough? In this paper we propose a method of attempting to prevent duplicate bug reports before they start: continuous querying. That is as the bug reporter types in their bug report their text is used to query the bug database to find duplicate or related bug reports. This continuous querying allows the reporter to be alerted to duplicate bug reports as they report the bug, rather than formulating queries to find the duplicate bug report. Thus this work ushers in a new way of evaluating bug report deduplication techniques, as well as a new kind of bug deduplication task. We show that simple IR measures show some promise for addressing this problem but also that further research is needed to refine this novel process that is integrate-able into modern bug report systems.

scholarly journals Stopping duplicate bug reports before they start with Continuous Querying for bug reports

2016 ◽  
Author(s):  
Abram Hindle
Keyword(s):  
Information Retrieval ◽  
Software Engineering ◽  
Search Engines ◽  
Indexing Methods ◽  
Bug Reports ◽  
Bug Report ◽  
Engineering Information ◽  
String Search ◽  
The Creation ◽  
Duplicate Bug Reports

Bug deduplication is a hot topic in software engineering information retrieval research, but it is often not deployed. Typically to de-duplicate bug reports developers rely upon the search capabilities of the bug report software they employ, such as Bugzilla, Jira, or Github Issues. These search capabilities range from simple SQL string search to IR-based word indexing methods employed by search engines. Yet too often these searches do very little to stop the creation of duplicate bug reports. Some bug trackers have more than 10\% of their bug reports marked as duplicate. Perhaps these bug tracker search engines are not enough? In this paper we propose a method of attempting to prevent duplicate bug reports before they start: continuous querying. That is as the bug reporter types in their bug report their text is used to query the bug database to find duplicate or related bug reports. This continuous querying allows the reporter to be alerted to duplicate bug reports as they report the bug, rather than formulating queries to find the duplicate bug report. Thus this work ushers in a new way of evaluating bug report deduplication techniques, as well as a new kind of bug deduplication task. We show that simple IR measures show some promise for addressing this problem but also that further research is needed to refine this novel process that is integrate-able into modern bug report systems.

Constructive Knowledge Management Model and Information Retrieval Methods for Software Engineering

2014 ◽  
pp. 253-269
Author(s):  
Zeyar Aung ◽  
Khine Khine Nyunt
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Software Engineering ◽  
Software Development ◽  
General Purpose ◽  
Theory And Practice ◽  
Manufacturing Plants ◽  
Development Organizations ◽  
Information Retrieval Methods ◽  
Short Time

In this chapter, the authors discuss two important trends in modern software engineering (SE) regarding the utilization of knowledge management (KM) and information retrieval (IR). Software engineering is a discipline in which knowledge and experience, acquired in the course of many years, play a fundamental role. For software development organizations, the main assets are not manufacturing plants, buildings, and machines, but the knowledge held by their employees. Software engineering has long recognized the need for managing knowledge and that the SE community could learn much from the KM community. The authors introduce the fundamental concepts of KM theory and practice and mainly discuss the aspects of knowledge management that are valuable to software development organizations and how a KM system for such an organization can be implemented. In addition to knowledge management, information retrieval (IR) also plays a crucial role in SE. IR is a study of how to efficiently and effectively retrieve a required piece of information from a large corpus of storage entities such as documents. As software development organizations grow larger and have to deal with larger numbers (probably millions) of documents of various types, IR becomes an essential tool for retrieving any piece of information that a software developer wants within a short time. IR can be used both as a general-purpose tool to improve the productivity of developers or as an enabler tool to facilitate a KM system.

Large-scale information retrieval in software engineering - an experience report from industrial application

2015 ◽  
Vol 21 (6) ◽  
pp. 2324-2365 ◽  
Author(s):  
Michael Unterkalmsteiner ◽  
Tony Gorschek ◽  
Robert Feldt ◽  
Niklas Lavesson
Keyword(s):  
Information Retrieval ◽  
Software Engineering ◽  
Industrial Application ◽  
Large Scale ◽  
Experience Report

scholarly journals Opportunities and Challenges in Code Search Tools

2022 ◽  
Vol 54 (9) ◽  
pp. 1-40
Author(s):  
Chao Liu ◽  
Xin Xia ◽  
David Lo ◽  
Cuiyun Gao ◽  
Xiaohu Yang ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Software Engineering ◽  
Software Development ◽  
Large Scale ◽  
Research Trends ◽  
Publication Trends ◽  
Code Search ◽  
Search Tasks ◽  
Efficiency And Effectiveness

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

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