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Adaptive User Feedback for IR-Based Traceability Recovery. / Panichella, Annibale; De Lucia, Andrea; Zaidman, Andy.

Proceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015. Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 15-21 7181523.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Panichella, A, De Lucia, A & Zaidman, A 2015, Adaptive User Feedback for IR-Based Traceability Recovery. in Proceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015., 7181523, Institute of Electrical and Electronics Engineers (IEEE), pp. 15-21, 8th IEEE/ACM International Symposium on Software and Systems Traceability, SST 2015, Florence, Italy, 17/05/15. https://doi.org/10.1109/SST.2015.10

APA

Panichella, A., De Lucia, A., & Zaidman, A. (2015). Adaptive User Feedback for IR-Based Traceability Recovery. In Proceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015 (pp. 15-21). [7181523] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SST.2015.10

Vancouver

Panichella A, De Lucia A, Zaidman A. Adaptive User Feedback for IR-Based Traceability Recovery. In Proceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015. Institute of Electrical and Electronics Engineers (IEEE). 2015. p. 15-21. 7181523 https://doi.org/10.1109/SST.2015.10

Author

Panichella, Annibale ; De Lucia, Andrea ; Zaidman, Andy. / Adaptive User Feedback for IR-Based Traceability Recovery. Proceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015. Institute of Electrical and Electronics Engineers (IEEE), 2015. pp. 15-21

BibTeX

@inproceedings{cef646eaee6e475aa2a84c6b1f46f62a,
title = "Adaptive User Feedback for IR-Based Traceability Recovery",
abstract = "Trace ability recovery allows software engineers to understand the interconnections among software artefacts and, thus, it provides an important support to software maintenance activities. In the last decade, Information Retrieval (IR) has been widely adopted as core technology of semi-automatic tools to extract trace ability links between artefacts according to their textual information. However, a widely known problem of IR-based methods is that some artefacts may share more words with non-related artefacts than with related ones. To overcome this problem, enhancing strategies have been proposed in literature. One of these strategies is relevance feedback, which allows to modify the textual similarity according to information about links classified by the users. Even though this technique is widely used for natural language documents, previous work has demonstrated that relevance feedback is not always useful for software artefacts. In this paper, we propose an adaptive version of relevance feedback that, unlike the standard version, considers the characteristics of both (i) the software artefacts and (ii) the previously classified links for deciding whether and how to apply the feedback. An empirical evaluation conducted on three systems suggests that the adaptive relevance feedback outperforms both a pure IR-based method and the standard feedback.",
keywords = "Empirical Software Engineering, Information Retrieval, Software Traceability, User Feedback Analysis",
author = "Annibale Panichella and {De Lucia}, Andrea and Andy Zaidman",
year = "2015",
month = "8",
day = "5",
doi = "10.1109/SST.2015.10",
language = "English",
pages = "15--21",
booktitle = "Proceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
address = "United States",

}

RIS

TY - GEN

T1 - Adaptive User Feedback for IR-Based Traceability Recovery

AU - Panichella, Annibale

AU - De Lucia, Andrea

AU - Zaidman, Andy

PY - 2015/8/5

Y1 - 2015/8/5

N2 - Trace ability recovery allows software engineers to understand the interconnections among software artefacts and, thus, it provides an important support to software maintenance activities. In the last decade, Information Retrieval (IR) has been widely adopted as core technology of semi-automatic tools to extract trace ability links between artefacts according to their textual information. However, a widely known problem of IR-based methods is that some artefacts may share more words with non-related artefacts than with related ones. To overcome this problem, enhancing strategies have been proposed in literature. One of these strategies is relevance feedback, which allows to modify the textual similarity according to information about links classified by the users. Even though this technique is widely used for natural language documents, previous work has demonstrated that relevance feedback is not always useful for software artefacts. In this paper, we propose an adaptive version of relevance feedback that, unlike the standard version, considers the characteristics of both (i) the software artefacts and (ii) the previously classified links for deciding whether and how to apply the feedback. An empirical evaluation conducted on three systems suggests that the adaptive relevance feedback outperforms both a pure IR-based method and the standard feedback.

AB - Trace ability recovery allows software engineers to understand the interconnections among software artefacts and, thus, it provides an important support to software maintenance activities. In the last decade, Information Retrieval (IR) has been widely adopted as core technology of semi-automatic tools to extract trace ability links between artefacts according to their textual information. However, a widely known problem of IR-based methods is that some artefacts may share more words with non-related artefacts than with related ones. To overcome this problem, enhancing strategies have been proposed in literature. One of these strategies is relevance feedback, which allows to modify the textual similarity according to information about links classified by the users. Even though this technique is widely used for natural language documents, previous work has demonstrated that relevance feedback is not always useful for software artefacts. In this paper, we propose an adaptive version of relevance feedback that, unlike the standard version, considers the characteristics of both (i) the software artefacts and (ii) the previously classified links for deciding whether and how to apply the feedback. An empirical evaluation conducted on three systems suggests that the adaptive relevance feedback outperforms both a pure IR-based method and the standard feedback.

KW - Empirical Software Engineering

KW - Information Retrieval

KW - Software Traceability

KW - User Feedback Analysis

UR - http://www.scopus.com/inward/record.url?scp=84955489427&partnerID=8YFLogxK

U2 - 10.1109/SST.2015.10

DO - 10.1109/SST.2015.10

M3 - Conference contribution

SP - 15

EP - 21

BT - Proceedings - 2015 IEEE/ACM 8th International Symposium on Software and Systems Traceability, SST 2015

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

ID: 47052862