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
Kebanyakan kaedah anggaran usaha sedia ada dalam pembangunan perisian adalah manual, intensif buruh dan subjektif, mengakibatkan anggaran terlalu tinggi dengan pembidaan gagal, dan anggaran rendah dengan kerugian wang. Kertas kerja ini menyiasat keberkesanan model jujukan dalam menganggar usaha pembangunan, dalam bentuk bulan-manusia, daripada data projek perisian. Empat seni bina; (1) Purata vektor perkataan dengan Multi-layer Perceptron (MLP), (2) Purata perkataan-vektor dengan Regresi Vektor Sokongan (SVR), (3) Model jujukan Unit Berulang Berpagar (GRU) dan (4) Panjang pendek- model jujukan ingatan jangka (LSTM) dibandingkan dari segi perbezaan manusia-bulan. Pendekatan dinilai menggunakan dua set data; ISEM (1,573 perihalan projek perisian Inggeris) dan ISBSG (9,100 data projek perisian), di mana yang pertama ialah teks mentah dan yang kedua ialah jadual data berstruktur menerangkan ciri projek perisian. Model jujukan LSTM mencapai ralat mutlak min terendah dan kedua terendah, iaitu 0.705 dan 14.077 bulan manusia untuk set data ISEM dan ISBSG masing-masing. Model MLP mencapai min ralat mutlak terendah iaitu 14.069 untuk set data ISBSG.
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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Salinan
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
Salinan
@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},}
Salinan
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 -