IEICE TRANSACTIONS on Information
Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.4 pp.739-747
- Publication Date
- 2020/04/01
- Publicized
- 2020/01/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019IIP0014
- Type of Manuscript
- Special Section PAPER (Special Section on Intelligent Information and Communication Technology and its Applications to Creative Activity Support)
- Category
Authors
Tachanun KANGWANTRAKOOL
Sirindhorn International Institute of Technology
Kobkrit VIRIYAYUDHAKORN
Sirindhorn International Institute of Technology
Thanaruk THEERAMUNKONG
Sirindhorn International Institute of Technology
Keyword
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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},}
Copy
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 -
English
