Scaling up analogy-based software effort estimation : a comparison of multiple hadoop implementation schemes

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review

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Original languageEnglish
Publication statusPublished - 16 Nov 2014

Conference

TitleInnovative Software Development (InnoSWDev) at Foundation of Software Engineering Conference 2014
PlaceChina
CityHong Kong
Period16 - 21 November 2014

Abstract

Analogy-based estimation (ABE) is one of the most time consuming and compute intensive method in software development effort estimation. Optimizing ABE has been a dilemma because simplifying the procedure can reduce the estimation performance, while increasing the procedure complexity with more sophisticated theory may sacrifice an advantage 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 instances 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 provide 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.

Citation Format(s)

Scaling up analogy-based software effort estimation : a comparison of multiple hadoop implementation schemes. / Passakorn, Phannachitta; KEUNG, Wai Jacky; Akito, Monden; MATSUMOTO, Kenichi.

2014. Paper presented at Innovative Software Development (InnoSWDev) at Foundation of Software Engineering Conference 2014, Hong Kong, China.

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)32_Refereed conference paper (no ISBN/ISSN)peer-review