Software Development Effort Estimation from Unstructured Software Project Description by Sequence Models


IEICE TRANSACTIONS on Information and Systems   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)
software effort estimation,  regression,  deep learning,  sequence model,  recurrent neural network (RNN),  gated recurrent units (GRU),  long short-term memory network (LSTM),  

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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.