JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2022 Vol.23 No.5 P.664-677

An incremental software architecture recovery technique driven by code changes

Author(s): Li WANG, Xianglong KONG, Jiahui WANG, Bixin LI
Affiliation(s): School of Computer Science and Engineering, Southeast University, Nanjing 210096, China; more
Corresponding email(s): wangli1218@seu.edu.cn, xlkong@seu.edu.cn, 18262609320@163.com, bx.li@seu.edu.cn
Key Words: Architecture recovery, Software evolution, Code change

Share this article to： More <<< Previous Article \|Next Article >>>

Abstract: It is difficult to keep software architecture up to date with code changes during software evolution. Inconsistency is caused by the limitations of standard development specifications and human power resources, which may impact software maintenance. To solve this problem, we propose an incremental software architecture recovery (ISAR) technique. Our technique obtains dependency information from changed code blocks and identifies different strength-level dependencies. Then, we use double classifiers to recover the architecture based on the method of mapping code-level changes to architecture-level updates. ISAR is evaluated on 10 open-source projects, and the results show that it performs more effectively and efficiently than the compared techniques. We also find that the impact of low-quality architectural documentation on effectiveness remains stable during software evolution.

代码变更驱动的增量式软件架构恢复技术

王丽^1,2，孔祥龙¹，王家慧³，李必信¹
¹东南大学计算机科学与工程学院，中国南京市，210096
²江苏自动化研究所，中国连云港市，222061
³华为数字技术实验室，中国苏州市，215125
摘要：在软件演化过程中，受开发能力和投入资源限制，软件架构通常难以与代码保持同步更新，导致架构设计与代码产生不一致，对软件维护等工作造成潜在影响。为解决此问题，本文提出一种增量式软件架构恢复技术，即ISAR。该技术首先从变更代码片段中提取依赖信息，然后根据依赖强度分析模块间关联关系，最后基于代码变更与架构更新间的关联关系设计两层分类器以恢复架构。本文基于10个开源项目构建验证实验，结果表明ISAR在架构恢复精度和效率方面优于传统技术。此外，本文发现架构设计文档质量对ISAR架构恢复精度有一定影响，但随着版本迭代逐渐趋于稳定。

关键词：架构恢复；软件演化；代码变更

Reference

[1]Akthar S, Rafi S, 2010. Recovery of software architecture using partitioning approach by Fiedler vector and clustering. Comput Inform Sci, 3(1):72-75.

[2]Ali S, Maqbool O, 2009. Monitoring software evolution using multiple types of changes. Int Conf on Emerging Technologies, p.410-415.

[3]Andritsos P, Tzerpos V, 2005. Information-theoretic software clustering. IEEE Trans Softw Eng, 31(2):150-165.

[4]Anquetil N, Lethbridge TC, 2003. Comparative study of clustering algorithms and abstract representations for software remodularisation. IEE Proc Softw, 150(3):185-201.

[5]Bazylevych R, Burtnyk R, 2015. Algorithms for software clustering and modularization. X^th Int Scientific and Technical Conf “Computer Sciences and Information Technologies”, p.30-33.

[6]Bittencourt RA, Guerrero DDS, 2009. Comparison of graph clustering algorithms for recovering software architecture module views. 13^th European Conf on Software Maintenance and Reengineering, p.251-254.

[7]Campo M, Amandi A, Biset JC, 2021. A software architecture perspective about Moodle flexibility for supporting empirical research of teaching theories. Educ Inform Technol, 26(1):817-842.

[8]Cho C, Lee KS, Lee M, et al., 2019. Software architecture module-view recovery using cluster ensembles. IEEE Access, 7:72872-72884.

[9]Garcia J, Ivkovic I, Medvidovic N, 2013a. A comparative analysis of software architecture recovery techniques. 28^th IEEE/ACM Int Conf on Automated Software Engineering, p.486-496.

[10]Garcia J, Krka I, Mattmann C, et al., 2013b. Obtaining ground-truth software architectures. 35^th Int Conf on Software Engineering, p.901-910.

[11]Glukhikh MI, Itsykson VM, Tsesko VA, 2012. Using dependencies to improve precision of code analysis. Autom Contr Comput Sci, 46(7):338-344.

[12]Jia XY, Chen SQ, Zhou XQ, et al., 2021. Where to handle an exception? Recommending exception handling locations from a global perspective. IEEE/ACM 29^th Int Conf on Program Comprehension, p.369-380.

[13]Kobayashi K, Kamimura M, Kato K, et al., 2012. Feature-gathering dependency-based software clustering using Dedication and Modularity. 28^th IEEE Int Conf on Software Maintenance, p.462-471.

[14]Kong XL, Li BX, Wang LL, et al., 2018. Directory-based dependency processing for software architecture recovery. IEEE Access, 6:52321-52335.

[15]Kong XL, Han WN, Liao L, et al., 2020. An analysis of correctness for API recommendation: are the unmatched results useless? Sci China Inform Sci, 63(9):190103.

[16]Lee KS, Lee CG, 2020. Identifying semantic outliers of source code artifacts and their application to software architecture recovery. IEEE Access, 8:212467-212477.

[17]Lehman MM, 1996. Laws of software evolution revisited. 5^th European Workshop Software Process Technology, p.108-124.

[18]Lima C, Assunção WK, Martinez J, et al., 2019. Product line architecture recovery with outlier filtering in software families: the Apo-Games case study. J Braz Comput Soc, 25(1):7.

[19]Link D, Behnamghader P, Moazeni R, et al., 2019. The value of software architecture recovery for maintenance. Proc 12^th Innovations on Software Engineering Conf (formerly known as India Software Engineering Conf), Article 17.

[20]Link D, Srisopha K, Boehm B, 2021. Study of the utility of text classification based software architecture recovery method RELAX for maintenance. Proc 15^th ACM/IEEE Int Symp on Empirical Software Engineering and Measurement, Article 33.

[21]Liu X, Wang HD, Ma HY, et al., 2021. The architecture design and implementation of aircraft structural fault assistant decision system based on data analysis. J Phys Conf Ser, 1813:012032.

[22]Lutellier T, Chollak D, Garcia J, et al., 2015. Comparing software architecture recovery techniques using accurate dependencies. IEEE/ACM 37^th IEEE Int Conf on Software Engineering, p.69-78.

[23]Lutellier T, Chollak D, Garcia J, et al., 2018. Measuring the impact of code dependencies on software architecture recovery techniques. IEEE Trans Softw Eng, 44(2):159-181.

[24]Mancoridis S, Mitchell BS, Rorres C, et al., 1998. Using automatic clustering to produce high-level system organizations of source code. Proc 6^th Int Workshop on Program Comprehension, p.45-52.

[25]Mancoridis S, Mitchell BS, Chen Y, et al., 1999. Bunch: a clustering tool for the recovery and maintenance of software system structures. Proc IEEE Int Conf on Software Maintenance, p.50-59.

[26]Maqbool O, Babri HA, 2004. The weighted combined algorithm: a linkage algorithm for software clustering. 8^th European Conf on Software Maintenance and Reengineering, p.15-24.

[27]Maqbool O, Babri HA, 2007. Bayesian learning for software architecture recovery. Int Conf on Electrical Engineering, p.1-6.

[28]Mendonça NC, Kramer J, 1998. An experiment in distributed software architecture recovery. 2^nd Int ESPRIT ARES Workshop on Development and Evolution of Software Architectures for Product Families, p.106-114.

[29]Mens T, Tourwe T, 2004. A survey of software refactoring. IEEE Trans Softw Eng, 30(2):126-139.

[30]Mitchell BS, 2003. A heuristic approach to solving the software clustering problem. Int Conf on Software Maintenance, p.285-288.

[31]Mitchell BS, Mancoridis S, 2006. On the automatic modularization of software systems using the Bunch tool. IEEE Trans Softw Eng, 32(3):193-208.

[32]Monroy M, Pinzger M, 2021. ARCo: architecture recovery in context. J Xi'an Univ Arch Technol, XIII(2):128.

[33]Naseem R, Maqbool O, Muhammad S, 2013. Cooperative clustering for software modularization. J Syst Softw, 86(8):2045-2062.

[34]Pourasghar B, Izadkhah H, Isazadeh A, et al., 2021. A graph-based clustering algorithm for software systems modularization. Inform Softw Technol, 133:106469.

[35]Sartipi K, 2003. Software architecture recovery based on pattern matching. Int Conf on Software Maintenance, p.293-296.

[36]Schmitt Laser M, Medvidovic N, Le DM, et al., 2020. ARCADE: an extensible workbench for architecture recovery, change, and decay evaluation. Proc 28^th ACM Joint Meeting on European Software Engineering Conf and Symp on the Foundations of Software Engineering, p.1546-1550.

[37]Sievi-Korte O, Richardson I, Beecham S, 2019. Software architecture design in global software development: an empirical study. J Syst Softw, 158:110400.

[38]Silva DEU, Bittencourt RA, Calumby RT, 2019. Clustering similarity measures for architecture recovery of evolving software. Anais do VII Workshop de Visualização, Evolução E Manutenção de Software, p.45-52.

[39]Sözer H, 2019. Evaluating the effectiveness of multi-level greedy modularity clustering for software architecture recovery. 13^th European Conf on Software Architecture, p.71-87.

[40]Tamburri DA, Kazman R, 2018. General methods for software architecture recovery: a potential approach and its evaluation. Empir Softw Eng, 23(3):1457-1489.

[41]Teymourian N, Izadkhah H, Isazadeh A, 2020. A fast clustering algorithm for modularization of large-scale software systems. IEEE Trans Softw Eng, early access.

[42]Tufano M, Sajnani H, Herzig K, 2019. Towards predicting the impact of software changes on building activities. IEEE/ACM ^41st Int Conf on Software Engineering, p.49-52.

[43]Tzerpos V, Holt RC, 2000. ACCD: an algorithm for comprehension-driven clustering. Proc 7^th Working Conf on Reverse Engineering, p.258-267.

[44]Wu J, Hassan AE, Holt RC, 2005. Comparison of clustering algorithms in the context of software evolution. 21^st IEEE Int Conf on Software Maintenance, p.525-535.

[45]Yang TF, Jiang ZY, Shang YH, et al., 2021. Systematic review on next-generation web-based software architecture clustering models. Comput Commun, 167:63-74.

[46]Zhang PL, Jiang YJ, Wei AJ, et al., 2021. Domain-specific fixes for flaky tests with wrong assumptions on underdetermined specifications. IEEE/ACM 43^rd Int Conf on Software Engineering, p.50-61.

[47]Zhao JF, Zhou JT, Yang HJ, et al., 2015. An orthogonal approach to reusable component discovery in cloud migration. China Commun, 12(5):134-151.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Similar articles

- Go to

代码变更驱动的增量式软件架构恢复技术

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference