Using english to retrieve software

Girardi, M.; Ibrahim, Bertrand

doi:10.1016/0164-1212(94)00137-c

Cited by 38 publications

(18 citation statements)

References 8 publications

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“…Unlike the approaches mentioned above, domain information, developer context and component relationships are all considered. Empirical results indicate that such schemes are superior to traditional approaches (Girardi and Ibrahim, 1995;Sugumaran and Storey, 2003;Yao and Etzkorn, 2004).…”

Section: Related Workmentioning

confidence: 88%

Rascal: A Recommender Agent for Agile Reuse

2005

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As software organisations mature, their repositories of reusable software components from previous projects will also grow considerably. Remaining conversant with all components in such a repository presents a significant challenge to developers. Indeed the retrieval of a particular component in this large search space may prove problematic. Further to this, the reuse of components developed in an Agile environment is likely to be hampered by the existence of little or no support materials. We propose to infer the need for a component and proactively recommend that component to the developer using a technique which is consistent with the principles of Agile methodologies. Our RASCAL recommender agent tracks usage histories of a group of developers to recommend to an individual developer components that are expected to be needed by that developer. Unlike many traditional recommender systems, we may recommend items that the developer has actually employed previously. We introduce a content-based filtering technique for ordering the set of recommended software components and present a comparative analysis of applying this technique to a number of collaborative filtering algorithms. We also investigate the relationship between the number of usage histories collected and recommendation accuracy. Our overall results indicate that RASCAL is a very promising tool for allowing developers discover reusable components at no additional cost.

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Section: Related Workmentioning

confidence: 88%

Rascal: A Recommender Agent for Agile Reuse

2005

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“…The ROSA system (Girardi and Ibrahim 1995) uses a semantic case similarity measure to retrieve software components. The approach described in Girardi and Ibrahim (1995) uses semantic cases to represent natural language queries and natural language descriptions of software components in an information retrieval system.…”

Section: Related Workmentioning

confidence: 99%

“…Then a semantic case similarity measure similar to the one described in (Girardi and Ibrahim 1995) is used to compare the semantic cases associated with the information items (i.e. the legal branches associated to the normative instruments) with the ones associated with the legal users.…”

Section: Multi-agent Society Modelingmentioning

confidence: 99%

A multi-agent legal recommender system

Drumond

Girardi

2008

Artif Intell Law

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Infonorma is a multi-agent system that provides its users with recommendations of legal normative instruments they might be interested in. The Filter agent of Infonorma classifies normative instruments represented as Semantic Web documents into legal branches and performs content-based similarity analysis. This agent, as well as the entire Infonorma system, was modeled under the guidelines of MAAEM, a software development methodology for multi-agent application engineering. This article describes the Infonorma requirements specification, the architectural design solution for those requirements, the detailed design of the Filter agent and the implementation model of Infonorma, according to the guidelines of the MAAEM methodology.

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“…Another informal method includes natural language processing in queries for the retrieval of software components. Such techniques include semantic networks and free-text analysis for automatic keyword extraction (e.g., Girardi & Ibrahim, 1995;Sugumaran & Storey, 2003;Yao & Etzkorn, 2004). Formal methods, alternatively, aim to cluster and classify components by providing a specification for software components (Chu et al, 2000;Nakkrasae & Sophatsathit, 2002).…”

Section: Previous Attempts Of Component Clustering Search and Retrievalmentioning

confidence: 99%