UZH-Logo

Maintenance Infos

Supporting developers with natural language queries


Würsch, M; Ghezzi, G; Reif, G; Gall, H C (2010). Supporting developers with natural language queries. In: 32nd ACM/IEEE International Conference on Software Engineering, Cape Town, South Africa, 2 May 2010 - 2 May 2010, 165-174.

Abstract

The feature list of modern IDEs is growing steadily and mastering these tools becomes more and more demanding, especially for novice programmers. Despite their remarkable capabilities, IDEs often still cannot directly answer the questions that arise during program comprehension tasks. Instead developers have to map their questions to multiple concrete queries that can be answered only by combining several tools and examining the output of each of them manually to distill an appropriate answer. Existing approaches have in common that they are either limited to a set of predefined, hardcoded questions, or that they require to learn a specific query language only suitable for that limited purpose. We present a framework to query for information about a software system using guided-input natural language resembling plain English. For that, we model data extracted by classical software analysis tools with an OWL ontology and use knowledge processing technologies from the Semantic Web to query it. We also present a case study that demonstrates how our framework can be used to answer queries about static source code information for program comprehension purposes.

The feature list of modern IDEs is growing steadily and mastering these tools becomes more and more demanding, especially for novice programmers. Despite their remarkable capabilities, IDEs often still cannot directly answer the questions that arise during program comprehension tasks. Instead developers have to map their questions to multiple concrete queries that can be answered only by combining several tools and examining the output of each of them manually to distill an appropriate answer. Existing approaches have in common that they are either limited to a set of predefined, hardcoded questions, or that they require to learn a specific query language only suitable for that limited purpose. We present a framework to query for information about a software system using guided-input natural language resembling plain English. For that, we model data extracted by classical software analysis tools with an OWL ontology and use knowledge processing technologies from the Semantic Web to query it. We also present a case study that demonstrates how our framework can be used to answer queries about static source code information for program comprehension purposes.

Citations

Altmetrics

Downloads

112 downloads since deposited on 28 Feb 2011
17 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:2 May 2010
Deposited On:28 Feb 2011 09:06
Last Modified:05 Apr 2016 14:36
Free access at:Related URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1145/1806799.1806827
Related URLs:http://www.ifi.uzh.ch/pax/index.php/publication/show/id/1272 (Author)
Permanent URL: https://doi.org/10.5167/uzh-42655

Download

[img]
Preview
Filetype: PDF
Size: 416kB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations