Scaling up analogy-based software effort estimation : A Comparison of multiple hadoop implementation schemes

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Passakorn Phannachitta
  • Jacky Keung
  • Akito Monden
  • Kenichi Matsumoto

Detail(s)

Original languageEnglish
Title of host publicationInternational Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings
PublisherAssociation for Computing Machinery, Inc
Pages65-72
ISBN (Print)9781450332262
Publication statusPublished - 16 Nov 2014
Externally publishedYes

Conference

TitleInternational Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014
PlaceChina
CityHong Kong
Period16 November 2014

Abstract

Analogy-based estimation (ABE) is one of the most time consuming and compute intensive method in software de- velopment effort estimation. Optimizing ABE has been a dilemma because simplifying the procedure can reduce the estimation performance, while increasing the procedure com- plexity with more sophisticated theory may sacrifice an ad- vantage of the unlimited scalability for a large data input. Motivated by an emergence of cloud computing technology in software applications, in this study we present 3 different implementation schemes based on Hadoop MapReduce to optimize the ABE process across multiple computing in- stances in the cloud-computing environment. We experimentally compared the 3 MapReduce implementation schemes in contrast with our previously proposed GPGPU approach (named ABE-CUDA) over 8 high-performance Amazon EC2 instances. Results present that the Hadoop solution can pro- vide more computational resources that can extend the scalability of the ABE process. We recommend adoption of 2 different Hadoop implementations (Hadoop streaming and RHadoop) for accelerating the computation specifically for compute-intensive software engineering related tasks.

Research Area(s)

  • Analogy-based estimation, Cloud computing, CUDA, Map reduce, Software effort estimation

Citation Format(s)

Scaling up analogy-based software effort estimation : A Comparison of multiple hadoop implementation schemes. / Phannachitta, Passakorn; Keung, Jacky; Monden, Akito; Matsumoto, Kenichi.

International Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings. Association for Computing Machinery, Inc, 2014. p. 65-72.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review