Conference proceeding
Exploring the Architectural Impact of Possible Dependencies in Python Software
2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020), pp 758-770
01 Jan 2020
Abstract
Dependencies among software entities are the basis for many software analytic research and architecture analysis tools. Dynamically typed languages, such as Python, JavaScript and Ruby, tolerate the lack of explicit type references, making certain syntactic dependencies indiscernible in source code. We call these possible dependencies, in contrast with the explicit dependencies that are directly referenced in source code. Type inference techniques have been widely studied and applied, but existing architecture analytic research and tools have not taken possible dependencies into consideration. The fundamental question is, to what extent will these missing possible dependencies impact the architecture analysis? To answer this question, we conducted an empirical study with 105 Python projects, using type inference techniques to manifest possible dependencies. Our study revealed that the architectural impact of possible dependencies is substantial-higher than that of explicit dependencies: (1) file-level possible dependencies account for at least 27.93% of all file-level dependencies, and create different dependency structures than that of explicit dependencies only, with an average difference of 30.71%; (2) adding possible dependencies significantly improves the precision (0.52%similar to 14.18%), recall(31.73%similar to 39.12%), and F1 scores (22.13%similar to 32.09%) of capturing co-change relations; (3) on average, a file involved in possible dependencies influences 28% more files and 42% more dependencies within architectural sub-spaces than a file involved in just explicit dependencies; (4) on average, a file involved in possible dependencies consumes 32% more maintenance effort. Consequently, maintainability scores reported by existing tools make a system written in these dynamic languages appear to be better modularized than it actually is. This evidence strongly suggests that possible dependencies have a more significant impact than explicit dependencies on architecture quality, that architecture analysis and tools should assess and even emphasize the architectural impact of possible dependencies due to dynamic typing.
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Details
- Title
- Exploring the Architectural Impact of Possible Dependencies in Python Software
- Creators
- Wuxia Jin - Xi'an Jiaotong UniversityYuanfang Cai - Drexel UniversityRick Kazman - Honolulu UniversityGang Zhang - Emergent Design Inc, Shanghai, ChinaQinghua Zheng - Xi'an Jiaotong UniversityTing Liu - Xi'an Jiaotong UniversityIEEE Comp Soc
- Publication Details
- 2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020), pp 758-770
- Series
- IEEE ACM International Conference on Automated Software Engineering
- Publisher
- IEEE
- Number of pages
- 13
- Grant note
- 2018YFB0803501 / National Key R&D Program of China 61632015; 61772408; U1766215; 61721002; 61532015; 61833015 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) IRT_17R86 / Ministry of Education Innovation Research Team 1817267; 1816594; 1823177; 1835292 / U.S. National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000651313500064
- Scopus ID
- 2-s2.0-85099217250
- Other Identifier
- 991019167519504721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Engineering, Electrical & Electronic