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

Passakorn Phannachitta, Jacky Keung, Akito Monden, Kenichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationInternational Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings
PublisherAssociation for Computing Machinery, Inc
Pages65-72
Number of pages8
ISBN (Print)9781450332262
DOIs
Publication statusPublished - Nov 16 2014
Externally publishedYes
EventInternational Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Hong Kong, China
Duration: Nov 16 2014 → …

Other

OtherInternational Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014
CountryChina
CityHong Kong
Period11/16/14 → …

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Keywords

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

ASJC Scopus subject areas

  • Software

Cite this

Phannachitta, P., Keung, J., Monden, A., & Matsumoto, K. (2014). Scaling up analogy-based software effort estimation: A Comparison of multiple hadoop implementation schemes. In International Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings (pp. 65-72). Association for Computing Machinery, Inc. https://doi.org/10.1145/2666581.2666582