article

Rascal: A Recommender Agent for Agile Reuse

Authors:

Mel Ó. Cinnéide,

Nicholas KushmerickAuthors Info & Claims

Artificial Intelligence Review, Volume 24, Issue 3-4

Pages 253 - 276

https://doi.org/10.1007/s10462-005-9012-8

Published: 01 November 2005 Publication History

Abstract

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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Cited By

Abid SAbdul Basit HShamail SRoychoudhury ACadar CKim M(2022)Context-aware code recommendation in Intellij IDEAProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3558937(1647-1651)Online publication date: 7-Nov-2022
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Jiang ZZhong HMeng N(2021)Investigating and recommending co-changed entities for JavaScript programsJournal of Systems and Software10.1016/j.jss.2021.111027180:COnline publication date: 1-Oct-2021
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Abid SShamail SBasit HNadi S(2021)FACER: An API usage-based code-example recommender for opportunistic reuseEmpirical Software Engineering10.1007/s10664-021-10000-w26:6Online publication date: 18-Aug-2021
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Index Terms

Rascal: A Recommender Agent for Agile Reuse
1. Software and its engineering
  1. Software creation and management
    1. Software development techniques
      1. Reusability
  2. Software notations and tools
    1. Software libraries and repositories

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Published In

cover image Artificial Intelligence Review

Artificial Intelligence Review Volume 24, Issue 3-4

Nov 2005

294 pages

ISSN:0269-2821

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Copyright © Copyright © 2005 Springer Science+Business Media, Inc.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2005

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Cited By

Abid SAbdul Basit HShamail SRoychoudhury ACadar CKim M(2022)Context-aware code recommendation in Intellij IDEAProceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3540250.3558937(1647-1651)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3540250.3558937
Jiang ZZhong HMeng N(2021)Investigating and recommending co-changed entities for JavaScript programsJournal of Systems and Software10.1016/j.jss.2021.111027180:COnline publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1016/j.jss.2021.111027
Abid SShamail SBasit HNadi S(2021)FACER: An API usage-based code-example recommender for opportunistic reuseEmpirical Software Engineering10.1007/s10664-021-10000-w26:6Online publication date: 18-Aug-2021
https://dl.acm.org/doi/10.1007/s10664-021-10000-w
Almonte LCantador IGuerra Ede Lara JGuerra EIovino L(2020)Towards automating the construction of recommender systems for low-code development platformsProceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings10.1145/3417990.3420200(1-10)Online publication date: 16-Oct-2020
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Chouchen MOuni AMkaouer M(2020)AndroLib: Third-Party Software Library Recommendation for Android ApplicationsReuse in Emerging Software Engineering Practices10.1007/978-3-030-64694-3_13(208-225)Online publication date: 2-Dec-2020
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Abid SDumas MPfahl DApel SRusso A(2019)Recommending related functions from API usage-based function clone structuresProceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3338906.3342486(1193-1195)Online publication date: 12-Aug-2019
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Eghan EAlqahtani SForbes CRilling J(2019)API trustworthiness: an ontological approach for software library adoptionSoftware Quality Journal10.1007/s11219-018-9428-427:3(969-1014)Online publication date: 1-Sep-2019
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Katsuragawa DIhara AKula RMatsumoto KAdams BConstantinou EMens TRobles G(2018)Maintaining third-party libraries through domain-specific category recommendationsProceedings of the 1st International Workshop on Software Health10.1145/3194124.3194129(2-9)Online publication date: 28-May-2018
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Cafaro LFrancese RPalumbo CRisi MTortora GHaddad HWainwright RChbeir R(2018)An agile process supporting software reuseProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167297(1544-1551)Online publication date: 9-Apr-2018
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Ouni AKula RKessentini MIshio TGerman DInoue K(2017)Search-based software library recommendation using multi-objective optimizationInformation and Software Technology10.1016/j.infsof.2016.11.00783:C(55-75)Online publication date: 1-Mar-2017
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