ICSE 2024
Fri 12 - Sun 21 April 2024 Lisbon, Portugal
Fri 19 Apr 2024 14:45 - 15:00 at Amália Rodrigues - Analytics 5 Chair(s): Sridhar Chimalakonda

Reliable effort estimation is of paramount importance to software planning and management, especially in industry that requires effective and on-time delivery. \textcolor{blue}{Various estimation approaches have been proposed (\eg planning poker and analogy), but they may be subjective and non-automatic, which are not applicable to other projects. \textcolor{violet}{(C${\textbf{A.19}}$, C${\textbf{B.5}}$)}} In recent years, deep learning approaches for effort estimation that rely on learning expert features or semantic features respectively have been extensively studied and have been found to be promising. Semantic features and expert features describe software tasks from different perspectives, however, in the literature, the best combination of these two features has not been explored to enhance effort estimation. Additionally, there are a few studies that discussed which expert features are useful for estimating effort in the industry. To this end, we investigate the potential 13 expert features that can be used to estimate effort by interviewing 26 enterprise employees. After that, we propose a novel model, called Fine-SE, that leverages semantic features and expert features for effort estimation. To validate our model, a series of evaluations are conducted on more than 30,000 software tasks from 17 industrial projects of a global ICT enterprise and four open-source software (OSS) projects. The evaluation results indicate that Fine-SE provides higher performance than the baselines on evaluation measures (\ie mean absolute error, mean magnitude of relative error, and performance indicator), particularly in industrial projects with large amounts of software tasks, which is a significant improvement in effort estimation. In comparison with expert estimation, Fine-SE improves the performance of evaluation measures by 32.0%-45.2% in within-project estimation. In comparison with the state-of-the-art models, Deep-SE and GPT2SP, it achieved an improvement of 8.9%-91.4% in industrial projects. The empirical results demonstrate the value of integrating expert features with semantic features in effort estimation.

Fri 19 Apr

Displayed time zone: Lisbon change

14:00 - 15:30
Analytics 5Research Track / Journal-first Papers at Amália Rodrigues
Chair(s): Sridhar Chimalakonda Indian Institute of Technology, Tirupati
14:00
15m
Talk
An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles
Research Track
Dinghua Wang , Shuqing Li The Chinese University of Hong Kong, Guanping Xiao Nanjing University of Aeronautics and Astronautics, Yepang Liu Southern University of Science and Technology, Yulei Sui UNSW, Pinjia He Chinese University of Hong Kong, Shenzhen, Michael Lyu The Chinese University of Hong Kong
14:15
15m
Talk
ModuleGuard: Understanding and Detecting Module Conflicts in Python Ecosystem
Research Track
Ruofan Zhu Zhejiang University, Xingyu Wang Zhejiang University, Chengwei Liu Nanyang Technological University, Zhengzi Xu Nanyang Technological University, Wenbo Shen Zhejiang University, China, Rui Chang Zhejiang University, Yang Liu Nanyang Technological University
14:30
15m
Talk
Empirical Analysis of Vulnerabilities Life Cycle in Golang Ecosystem
Research Track
Jinchang Hu , Lyuye Zhang Nanyang Technological University, Chengwei Liu Nanyang Technological University, Sen Yang Academy of Military Science, Song Huang Army Engineering University of PLA, Yang Liu Nanyang Technological University
14:45
15m
Talk
Fine-SE: Integrating Semantic Features and Expert Features for Software Effort Estimation
Research Track
Yue Li Nanjing University, Zhong Ren State Key Laboratory of Novel Software Technology, Software Institute, Nanjing University Nanjing, Jiangsu, China, Zhiqi Wang State Key Laboratory of Novel Software Technology, Software Institute, Nanjing University Nanjing, Jiangsu, China, Lanxin Yang Nanjing University, Liming Dong Nanjing University, He Zhang Nanjing University
15:00
7m
Talk
Concretization of Abstract Traffic Scene Specifications Using Metaheuristic Search
Journal-first Papers
Aren Babikian McGill University, Oszkár Semeráth Budapest University of Technology and Economics, Daniel Varro Linköping University / McGill University
15:07
7m
Talk
Technical leverage analysis in the Python ecosystem
Journal-first Papers
Ranindya Paramitha University of Trento, Fabio Massacci University of Trento; Vrije Universiteit Amsterdam
15:14
7m
Talk
Automated Mapping of Adaptive App GUIs from Phones to TVs
Journal-first Papers
Han Hu Faculty of Information Technology, Monash University, ruiqi dong Swinburne University of Technology, John Grundy Monash University, Thai Minh Nguyen Monash University, huaxiao liu Jilin University, Chunyang Chen Monash University
Link to publication DOI Pre-print
15:21
7m
Talk
Assessing the Early Bird Heuristic (for Predicting Project Quality)
Journal-first Papers
Shrikanth N C Oracle America Inc, Tim Menzies North Carolina State University
Link to publication DOI Pre-print
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