The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
기존의 소프트웨어 개발 노력 추정 방법은 대부분 수동적이고 노동집약적이며 주관적이어서 입찰 실패로 인한 과대평가, 금전적 손실로 인한 과소평가가 발생합니다. 본 논문에서는 소프트웨어 프로젝트 데이터로부터 맨먼스 형태로 개발 노력을 추정하는 데 있어 시퀀스 모델의 효율성을 조사합니다. 1가지 아키텍처 (2) MLP(다층 퍼셉트론)를 사용한 평균 단어 벡터, (3) SVR(지원 벡터 회귀)을 사용한 평균 단어 벡터, (4) GRU(Gated Recurrent Unit) 시퀀스 모델 및 (1,573) Long short- LSTM(term memory) 시퀀스 모델은 인력-월 차이 측면에서 비교됩니다. 이 접근 방식은 두 가지 데이터 세트를 사용하여 평가됩니다. ISEM(9,100개의 영어 소프트웨어 프로젝트 설명)과 ISBSG(0.705개의 소프트웨어 프로젝트 데이터), 전자는 원시 텍스트이고 후자는 소프트웨어 프로젝트의 특성을 설명하는 구조화된 데이터 테이블입니다. LSTM 시퀀스 모델은 ISEM 및 ISBSG 데이터 세트에 대해 각각 14.077 및 14.069 인력월인 가장 낮은 평균 절대 오류와 두 번째로 낮은 평균 절대 오류를 달성합니다. MLP 모델은 ISBSG 데이터 세트에 대해 XNUMX인 가장 낮은 평균 절대 오류를 달성합니다.
Tachanun KANGWANTRAKOOL
Sirindhorn International Institute of Technology
Kobkrit VIRIYAYUDHAKORN
Sirindhorn International Institute of Technology
Thanaruk THEERAMUNKONG
Sirindhorn International Institute of Technology
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Tachanun KANGWANTRAKOOL, Kobkrit VIRIYAYUDHAKORN, Thanaruk THEERAMUNKONG, "Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 4, pp. 739-747, April 2020, doi: 10.1587/transinf.2019IIP0014.
Abstract: Most existing methods of effort estimations in software development are manual, labor-intensive and subjective, resulting in overestimation with bidding fail, and underestimation with money loss. This paper investigates effectiveness of sequence models on estimating development effort, in the form of man-months, from software project data. Four architectures; (1) Average word-vector with Multi-layer Perceptron (MLP), (2) Average word-vector with Support Vector Regression (SVR), (3) Gated Recurrent Unit (GRU) sequence model, and (4) Long short-term memory (LSTM) sequence model are compared in terms of man-months difference. The approach is evaluated using two datasets; ISEM (1,573 English software project descriptions) and ISBSG (9,100 software projects data), where the former is a raw text and the latter is a structured data table explained the characteristic of a software project. The LSTM sequence model achieves the lowest and the second lowest mean absolute errors, which are 0.705 and 14.077 man-months for ISEM and ISBSG datasets respectively. The MLP model achieves the lowest mean absolute errors which is 14.069 for ISBSG datasets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019IIP0014/_p
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@ARTICLE{e103-d_4_739,
author={Tachanun KANGWANTRAKOOL, Kobkrit VIRIYAYUDHAKORN, Thanaruk THEERAMUNKONG, },
journal={IEICE TRANSACTIONS on Information},
title={Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models},
year={2020},
volume={E103-D},
number={4},
pages={739-747},
abstract={Most existing methods of effort estimations in software development are manual, labor-intensive and subjective, resulting in overestimation with bidding fail, and underestimation with money loss. This paper investigates effectiveness of sequence models on estimating development effort, in the form of man-months, from software project data. Four architectures; (1) Average word-vector with Multi-layer Perceptron (MLP), (2) Average word-vector with Support Vector Regression (SVR), (3) Gated Recurrent Unit (GRU) sequence model, and (4) Long short-term memory (LSTM) sequence model are compared in terms of man-months difference. The approach is evaluated using two datasets; ISEM (1,573 English software project descriptions) and ISBSG (9,100 software projects data), where the former is a raw text and the latter is a structured data table explained the characteristic of a software project. The LSTM sequence model achieves the lowest and the second lowest mean absolute errors, which are 0.705 and 14.077 man-months for ISEM and ISBSG datasets respectively. The MLP model achieves the lowest mean absolute errors which is 14.069 for ISBSG datasets.},
keywords={},
doi={10.1587/transinf.2019IIP0014},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models
T2 - IEICE TRANSACTIONS on Information
SP - 739
EP - 747
AU - Tachanun KANGWANTRAKOOL
AU - Kobkrit VIRIYAYUDHAKORN
AU - Thanaruk THEERAMUNKONG
PY - 2020
DO - 10.1587/transinf.2019IIP0014
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2020
AB - Most existing methods of effort estimations in software development are manual, labor-intensive and subjective, resulting in overestimation with bidding fail, and underestimation with money loss. This paper investigates effectiveness of sequence models on estimating development effort, in the form of man-months, from software project data. Four architectures; (1) Average word-vector with Multi-layer Perceptron (MLP), (2) Average word-vector with Support Vector Regression (SVR), (3) Gated Recurrent Unit (GRU) sequence model, and (4) Long short-term memory (LSTM) sequence model are compared in terms of man-months difference. The approach is evaluated using two datasets; ISEM (1,573 English software project descriptions) and ISBSG (9,100 software projects data), where the former is a raw text and the latter is a structured data table explained the characteristic of a software project. The LSTM sequence model achieves the lowest and the second lowest mean absolute errors, which are 0.705 and 14.077 man-months for ISEM and ISBSG datasets respectively. The MLP model achieves the lowest mean absolute errors which is 14.069 for ISBSG datasets.
ER -