Today/future importance analysis

Yuanyuan Zhang; Enrique Alba; Juan J. Durillo; Sigrid Eldh; Mark Harman

doi:10.1145/1830483.1830733

Today/future importance analysis

Yuanyuan Zhang, Enrique Alba, Juan J. Durillo, Sigrid Eldh, Mark Harman

Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

16 Citations (Scopus)

Abstract

SBSE techniques have been widely applied to requirements selection and prioritization problems in order to ascertain a suitable set of requirements for the next release of a system. Unfortunately, it has been widely observed that requirements tend to be changed as the development process proceeds and what is suitable for today, may not serve well into the future. Though SBSE has been widely applied to requirements analysis, there has been no previous work that seeks to balance the requirements needs of today with those of the future. This paper addresses this problem. It introduces a multi-objective formulation of the problem which is implemented using multi-objective Pareto optimal evolutionary algorithms. The paper presents the results of experiments on both synthetic and real world data.

Original language	English
Title of host publication	Proceedings of the 12th annual conference on Genetic and evolutionary computation
Place of Publication	New York
Publisher	ACM
Pages	1357
ISBN (Electronic)	978-1-4503-0072-8
DOIs	https://doi.org/10.1145/1830483.1830733
Publication status	Published - 1 Jan 2010
Event	the 12th annual conference - Portland, Oregon, USA, United Kingdom Duration: 7 Jul 2010 → 11 Jul 2010

Conference

Conference	the 12th annual conference
Country/Territory	United Kingdom
City	Portland, Oregon, USA
Period	7/07/2010 → 11/07/2010

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10.1145/1830483.1830733

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title = "Today/future importance analysis",

abstract = "SBSE techniques have been widely applied to requirements selection and prioritization problems in order to ascertain a suitable set of requirements for the next release of a system. Unfortunately, it has been widely observed that requirements tend to be changed as the development process proceeds and what is suitable for today, may not serve well into the future. Though SBSE has been widely applied to requirements analysis, there has been no previous work that seeks to balance the requirements needs of today with those of the future. This paper addresses this problem. It introduces a multi-objective formulation of the problem which is implemented using multi-objective Pareto optimal evolutionary algorithms. The paper presents the results of experiments on both synthetic and real world data.",

author = "Yuanyuan Zhang and Enrique Alba and Durillo, {Juan J.} and Sigrid Eldh and Mark Harman",

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T1 - Today/future importance analysis

AU - Zhang, Yuanyuan

AU - Alba, Enrique

AU - Durillo, Juan J.

AU - Eldh, Sigrid

AU - Harman, Mark

PY - 2010/1/1

Y1 - 2010/1/1

N2 - SBSE techniques have been widely applied to requirements selection and prioritization problems in order to ascertain a suitable set of requirements for the next release of a system. Unfortunately, it has been widely observed that requirements tend to be changed as the development process proceeds and what is suitable for today, may not serve well into the future. Though SBSE has been widely applied to requirements analysis, there has been no previous work that seeks to balance the requirements needs of today with those of the future. This paper addresses this problem. It introduces a multi-objective formulation of the problem which is implemented using multi-objective Pareto optimal evolutionary algorithms. The paper presents the results of experiments on both synthetic and real world data.

AB - SBSE techniques have been widely applied to requirements selection and prioritization problems in order to ascertain a suitable set of requirements for the next release of a system. Unfortunately, it has been widely observed that requirements tend to be changed as the development process proceeds and what is suitable for today, may not serve well into the future. Though SBSE has been widely applied to requirements analysis, there has been no previous work that seeks to balance the requirements needs of today with those of the future. This paper addresses this problem. It introduces a multi-objective formulation of the problem which is implemented using multi-objective Pareto optimal evolutionary algorithms. The paper presents the results of experiments on both synthetic and real world data.

U2 - 10.1145/1830483.1830733

DO - 10.1145/1830483.1830733

M3 - Conference paper

SP - 1357

BT - Proceedings of the 12th annual conference on Genetic and evolutionary computation

PB - ACM

CY - New York

T2 - the 12th annual conference

Y2 - 7 July 2010 through 11 July 2010

ER -

Today/future importance analysis

Abstract

Conference

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