Kaitlynn Burgess
Tomas Cerny
ABSTRACT
Provenance tracking is used to record vital information such as user actions and the origin of data, but its potential has not been utilized with software architecture. Given the importance of provenance tracking, it can be seen as beneficial to understand the methods used to track this architecture evolution, as well as having methods to help visualize the architecture evolution. Throughout this paper, a systematic review is conducted addressing how provenance tracking can be used to track software architectural changes. Additionally, open-source provenance tracking tools, Trrack, ProvViewer, VisTrails, InDiProv, and GraphTrail are discussed to show how such functionality can be applied to visualize software architecture. In this study, we analyzed a final selection of 35 papers. Among these papers, we compile content from them to better understand the potential of how provenance tracking can be used to aid the visualization of software architecture. This analysis can be applied to existing provenance tracking visualization tools as well as benefit researchers or practitioners intending to maintain and trace software architecture.
- Idrees Ahmed, Abid Khan, Mansoor Ahmed, and Saif ur Rehman. [n. d.]. Order preserving secure provenance scheme for distributed networks. 82 ([n. d.]), 99--117. Google Scholar
Digital Library
- L. Bavoil, S.P. Callahan, P.J. Crossno, J. Freire, C.E. Scheidegger, C.T. Silva, and H.T. Vo. [n. d.]. VisTrails: enabling interactive multiple-view visualizations. In VIS 05. IEEE Visualization, 2005. (2005--10). 135--142. Google ScholarCross Ref
- Elisa Bertino, Gabriel Ghinita, Murat Kantarcioglu, Dang Nguyen, Jae Park, Ravi Sandhu, Salmin Sultana, Bhavani Thuraisingham, and Shouhuai Xu. [n. d.]. A roadmap for privacy-enhanced secure data provenance. 43, 3 ([n. d.]), 481--501. Google ScholarDigital Library
- C. Bier. [n. d.]. How usage control and provenance tracking get together - A data protection perspective. 13--17. Google ScholarDigital Library
- P. Bourhis, D. Deutch, and Y. Moskovitch. [n. d.]. Equivalence-Invariant Algebraic Provenance for Hyperplane Update Queries. 415--429. ISSN: 0730-8078. Google ScholarDigital Library
- Dai Chaofan, Zhang Ran, Li Pei, Wang Wenqian, and Cao Zewen. [n. d.]. A Minimal Attribute Set-oriented Data Provenance Method. In Proceedings of the International Conference on Big Data and Internet of Thing (2017-12-20) (BDIOT2017). Association for Computing Machinery, 1--5. Google ScholarDigital Library
- Troy Costa Kohwalter, Felipe Machado de Azeredo Figueira, Eduardo Assis de Lima Serdeiro, Jose Ricardo da Silva Junior, Leonardo Gresta Paulino Murta, and Esteban Walter Gonzalez Clua. [n. d.]. Understanding game sessions through provenance. 27 ([n. d.]), 110--127. Google ScholarCross Ref
- Zach Cutler, Kiran Gadhave, and Alexander Lex. [n. d.]. Trrack: A Library for Provenance-Tracking in Web-Based Visualizations. In 2020 IEEE Visualization Conference (VIS) (2020-10). 116--120. Google ScholarCross Ref
- Daniel Deutch, Yuval Moskovitch, and Val Tannen. [n. d.]. Provenance-based analysis of data-centric processes. 24, 4 ([n. d.]), 583--607. Google ScholarDigital Library
- Cody Dunne, Nathalie Henry Riche, Bongshin Lee, Ronald Metoyer, and George Robertson. [n. d.]. GraphTrail: analyzing large multivariate, heterogeneous networks while supporting exploration history. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2012-05-05). ACM, 1663--1672. Google ScholarDigital Library
- R. Genquan, Z. Li, W. Jianmin, and L. Yinbo. [n. d.]. One method for provenance tracking of product lifecycle data in collaborative service environment. 347--356. Google ScholarDigital Library
- Sandra Gesing, Malcolm Atkinson, Rosa Filgueira, Ian Taylor, Andrew Jones, Vlado Stankovski, Chee Sun Liew, Alessandro Spinuso, Gabor Terstyanszky, and Peter Kacsuk. [n. d.]. Workflows in a dashboard: a new generation of usability. In Proceedings of the 9th Workshop on Workflows in Support of Large-Scale Science (2014-11-16) (WORKS '14). IEEE Press, 82--93. Google ScholarDigital Library
- Eric Griffis, Paul Martin, and James Cheney. [n. d.]. Semantics and provenance for processing element composition in dispel workflows. In Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science (2013-11-17) (WORKS '13). Association for Computing Machinery, 38--47. Google ScholarDigital Library
- Mohamed Oussama Hassan and Henri Basson. [n. d.]. Tracing Software Architecture Change Using Graph Formalisms in Distributed Systems. In 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications (2008-04). 1--6. Google ScholarCross Ref
- Lianlian He, Peng Yue, Liping Di, Mingda Zhang, and Lei Hu. [n. d.]. Adding Geospatial Data Provenance into SDI---A Service-Oriented Approach. 8, 2 ([n. d.]), 926--936. Conference Name: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Google ScholarCross Ref
- Jingmei Hu, Jiwon Joung, Maia Jacobs, Krzysztof Z. Gajos, and Margo I. Seltzer. [n. d.]. Improving data scientist efficiency with provenance. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (2020-06-27) (ICSE '20). Association for Computing Machinery, 1086--1097. Google ScholarDigital Library
- C. Hänel, M. Khatami, T.W. Kuhlen, and B. Weyers. [n. d.]. Towards Multi-user Provenance Tracking of Visual Analysis Workflows over Multiple Applications. 23--27. Google ScholarCross Ref
- KarvounarakisGrigoris, GreenTodd J, IvesZachary G, and TannenVal. [n. d.]. Collaborative data sharing via update exchange and provenance. ([n. d.]). Publisher: ACM PUB27 New York, NY, USA. Google ScholarDigital Library
- Barbara Kitchenham, Pearl Brereton, David Budgen, Mark Turner, John Bailey, and Stephen Linkman. [n. d.]. Systematic literature reviews in software engineering-A systematic literature review. 51 ([n. d.]), 7--15. Google ScholarDigital Library
- Troy Kohwalter, Thiago Oliveira, Juliana Freire, Esteban Clua, and Leonardo Murta. [n. d.]. Prov Viewer: A Graph-Based Visualization Tool for Interactive Exploration of Provenance Data. Pages: 82. Google ScholarCross Ref
- Luka Lelovic, Michael Mathews, Amr Elsayed, Tomas Cerny, Karel Frajtak, Pavel Tisnovsky, and Davide Taibi. 2022. Architectural Languages in the Microservice Era: A Systematic Mapping Study. In Architectural Languages in the Microservice Era: A Systematic Mapping Study. Association for Computing Machinery, New York, NY, USA, 8. Google ScholarDigital Library
- Zitong Li, Xiang Cheng, Lixiao Sun, Ji Zhang, Bing Chen, and Weizhi Meng. [n. d.]. A Hierarchical Approach for Advanced Persistent Threat Detection with Attention-Based Graph Neural Networks. 2021 ([n. d.]). Google ScholarDigital Library
- Cong Liao and Anna Squicciarini. [n. d.]. Towards provenance-based anomaly detection in MapReduce. In Proceedings of the 15th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing (2015-05-04) (CCGRID '15). IEEE Press, 647--656. Google ScholarDigital Library
- P. Lüthi, T. Gagnaux, and M. Gygli. [n. d.]. Distributed ledger for provenance tracking of artificial intelligence assets. 576 LNCS ([n. d.]), 411--426. ISBN: 9783030425036. Google ScholarCross Ref
- Shiqing Ma, Yousra Aafer, Zhaogui Xu, Wen-Chuan Lee, Juan Zhai, Yingqi Liu, and Xiangyu Zhang. [n. d.]. LAMP: data provenance for graph based machine learning algorithms through derivative computation. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (2017-08-21) (ESEC/FSE 2017). Association for Computing Machinery, 786--797. Google ScholarDigital Library
- Andrius Merkys, Nicolas Mounet, Andrea Cepellotti, Nicola Marzari, Saulius Gražulis, and Giovanni Pizzi. [n. d.]. A posteriori metadata from automated provenance tracking: integration of AiiDA and TCOD. 9, 1 ([n. d.]), 56. Google ScholarCross Ref
- Sudha Ram and Jun Liu. [n. d.]. A Semantic Foundation for Provenance Management. 1, 1 ([n. d.]), 11--17. Google ScholarCross Ref
- Guillaume Rousseau, Roberto Di Cosmo, and Stefano Zacchiroli. [n. d.]. Software provenance tracking at the scale of public source code. 25, 4 ([n. d.]), 2930--2959. Google ScholarCross Ref
- Marten Sigwart, Michael Borkowski, Marco Peise, Stefan Schulte, and Stefan Tai. [n. d.]. Blockchain-based Data Provenance for the Internet of Things. In Proceedings of the 9th International Conference on the Internet of Things (2019-10-22). ACM, 1--8. Google ScholarDigital Library
- Jianwu Wang, Daniel Crawl, Shweta Purawat, Mai Nguyen, and Ilkay Altintas. [n. d.]. Big data provenance: Challenges, state of the art and opportunities. In 2015 IEEE International Conference on Big Data (Big Data) (2015-10). 2509--2516. Google ScholarDigital Library
- David Wilkinson, Luís Oliveira, Daniel Mossé, and Bruce Childers. [n. d.]. Software Provenance: Track the Reality Not the Virtual Machine. In Proceedings of the First International Workshop on Practical Reproducible Evaluation of Computer Systems (2018-06-11). ACM, 1--6. Google ScholarDigital Library
- Byron J. Williams and Jeffrey C. Carver. [n. d.]. Characterizing software architecture changes: A systematic review. 52, 1 ([n. d.]), 31--51. Google ScholarDigital Library
- Yinjun Wu, Val Tannen, and Susan B. Davidson. [n. d.]. PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (2020-06-11) (SIGMOD '20). Association for Computing Machinery, 447--462. Google ScholarDigital Library
- Yulai Xie, Dan Feng, Xuelong Liao, and Leihua Qin. [n. d.]. Efficient monitoring and forensic analysis via accurate network-attached provenance collection with minimal storage overhead. 26 ([n. d.]), 19--28. Google ScholarCross Ref
- Mingda Zhang, Peng Yue, Zhaoyan Wu, Danielle Ziebelin, Huayi Wu, and Chenxiao Zhang. [n. d.]. Model provenance tracking and inference for integrated environmental modelling. 96 ([n. d.]), 95--105. Google ScholarDigital Library
- Nan Zheng and Zachary G. Ives. [n. d.]. Compact, tamper-resistant archival of fine-grained provenance. 14, 4 ([n. d.]), 485--497. Google ScholarDigital Library
Index Terms
- Visualizing architectural evolution via provenance tracking: a systematic review
Recommendations
Using software evolution to focus architectural recovery
Ideally, a software project commences with requirements gathering and specification , reaches its major milestone with system implementation and delivery , and then continues, possibly indefinitely, into an operation and maintenance phase. The ...
A large-scale study of architectural evolution in open-source software systems
From its very inception, the study of software architecture has recognized architectural decay as a regularly occurring phenomenon in long-lived systems. Architectural decay is caused by repeated, sometimes careless changes to a system during its ...
Taming architectural evolution
ESEC/FSE-9: Proceedings of the 8th European software engineering conference held jointly with 9th ACM SIGSOFT international symposium on Foundations of software engineeringIn the world of software development everything evolves. So, then, do software architectures. Unlike source code, for which the use of a configuration management (CM) system is the predominant approach to capturing and managing evolution, approaches to ...
Comments