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 host publication) › peer-review
Author(s)
Detail(s)
Original language | English |
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Title of host publication | International Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings |
Publisher | Association for Computing Machinery, Inc |
Pages | 65-72 |
ISBN (Print) | 9781450332262 |
Publication status | Published - 16 Nov 2014 |
Externally published | Yes |
Conference
Title | International Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 |
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Place | China |
City | Hong Kong |
Period | 16 November 2014 |
Link(s)
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 et al.
International Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings. Association for Computing Machinery, Inc, 2014. p. 65-72.
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 host publication) › peer-review