Top

Empirical Software Engineering

Published in:

01-03-2023

Refactoring practices in the context of data-intensive systems

Authors: Biruk Asmare Muse, Foutse Khomh, Giuliano Antoniol

Published in: Empirical Software Engineering | Issue 2/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Developers often refactor code to improve the maintainability and comprehension of the software. There are many studies on refactoring activities in traditional software systems. However, refactoring in data-intensive systems is not well explored. Understanding the refactoring practices of developers is important to develop efficient tool support. We conducted a longitudinal study of refactoring activities in data-access classes using 29 SQL and NoSQL database based data-intensive systems. We investigated the prevalence, co-occurrence, and evolution of data-access refactorings, and the association of data-access refactorings with data-access smells. We also conducted a manual analysis of 500 samples of data-access refactoring instances to identify the functionalities of the code that are targeted by such refactorings. Furthermore, we analyzed 500 sample data-access refactoring commits to understand the context behind the applied refactorings and explored the characteristics and contribution of developers involved in the refactorings. We also conducted a developer survey to complement our analysis on the subject systems. Our results show that data-access refactorings are prevalent and different in type. Most of the data-access refactorings target codes that implement data fetching and insertion, but they mostly do not modify data-access queries. Most of the data-access refactorings are done when adding or modifying features and during bug fixes. data-access refactoring is often performed by developers with higher development and refactoring experience. Overall, the results show that data-access refactorings focus on improving the code quality but not optimizing the underlying data-access operations by fixing data-access smells. Hence, more work is needed from the research community on providing awareness and support to practitioners on the benefits of addressing data-access smells with refactorings.

previous article Automated variable renaming: are we there yet?

next article Vulnerability management in Linux distributions

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

https://www.mongodb.com/developer/products/mongodb/schema-design-anti-pattern-separating-data/

https://riak.com/posts/technical/riak-development-anti-patterns/index.html

https://bit.ly/39RXcPI

https://github.com/bio2rdf/bio2rdf-scripts

http://www.jnosql.org/docs/introduction.html

https://github.com/tsantalis/RefactoringMiner

https://dwheeler.com/sloccount/

https://bit.ly/36XxUyk

https://bit.ly/3EzrZ0P

https://bit.ly/3z7dXCg

https://bit.ly/3Jhq0Si

https://bit.ly/3qyWf75

https://bit.ly/3ewi3dU

https://bit.ly/3HhviLN

https://www.red-gate.com/hub/product-learning/sql-prompt

https://sqlenlight.com/

Agrawal R, Imielinski T, Swami A (1993) Mining associations between sets of items in large databases. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 207–216

Agrawal R, Srikant R, et al. (1994) Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB, vol 1215. Citeseer, pp 487–499

de Almeida Filho FG, Martins ADF, Vinuto TdS, Monteiro JM, de Sousa ÍP, de Castro Machado J, Rocha LS (2019) Prevalence of bad smells in PL/SQL projects. In: Proceedings of the 27th international conference on program comprehension. IEEE Press, pp 116–121

Alomar EA, Peruma A, Mkaouer MW, Newman C, Ouni A, Kessentini M (2021) How we refactor and how we document it? On the use of supervised machine learning algorithms to classify refactoring documentation. Expert Syst Appl 167(114):176

Arzamasova N, Schäler M, Böhm K (2018) Cleaning antipatterns in an SQL query log. IEEE Trans Knowl Data Eng 30(3):421–434CrossRef

Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pp 255–264

Chávez A, Ferreira I, Fernandes E, Cedrim D, Garcia A (2017) How does refactoring affect internal quality attributes? a multi-project study. In: Proceedings of the 31st Brazilian symposium on software engineering, pp 74–83

Chen CP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: A survey on big data. Inf Sci 275:314–347CrossRef

Cramer H (1946) Mathematical methods of statistics. Princeton U, Press, Princeton, p 500MATH

Dig D, Comertoglu C, Marinov D, Johnson R (2006) Automated detection of refactorings in evolving components. In: European conference on object-oriented programming. Springer, pp 404–428

Falleri JR, Morandat F, Blanc X, Martinez M, Monperrus M (2014) Fine-grained and accurate source code differencing. In: Proceedings of the 29th ACM/IEEE international conference on Automated software engineering, pp 313–324

Ferreira I, Fernandes E, Cedrim D, Uchôa A, Bibiano AC, Garcia A, Correia JL, Santos F, Nunes G, Barbosa C et al (2018) The buggy side of code refactoring: Understanding the relationship between refactorings and bugs. In: Proceedings of the 40th international conference on software engineering: companion proceeedings, pp 406–407

Foidl H, Felderer M, Biffl S (2019) Technical debt in data-intensive software systems. In: 2019 45th Euromicro conference on software engineering and advanced applications (SEAA). https://doi.org/10.1109/SEAA.2019.00058, pp 338–341

Foidl H, Felderer M, Ramler R (2022) Data smells: categories, causes and consequences, and detection of suspicious data in ai-based systems. In: Crnkovic I (ed) Proceedings of the 1st international conference on AI engineering: software engineering for AI, CAIN 2022, Pittsburgh, Pennsylvania, May 16-24, 2022. https://doi.org/10.1145/3522664.3528590. ACM, pp 229–239

Fowler M (2002) Refactoring: Improving the design of existing code. Extreme Program Agile Methods–XP/Agil Universe 2002:256CrossRefMATH

Hummel O, Eichelberger H, Giloj A, Werle D, Schmid K (2018) A collection of software engineering challenges for big data system development. In: 2018 44th Euromicro conference on software engineering and advanced applications (SEAA). https://doi.org/10.1109/SEAA.2018.00066, pp 362–369

Hummel O, Eichelberger H, Giloj A, Werle D, Schmid K (2018) A collection of software engineering challenges for big data system development. In: 2018 44th euromicro conference on software engineering and advanced applications (SEAA). IEEE, pp 362–369

Iammarino M, Zampetti F, Aversano L, Di Penta M (2019) Self-admitted technical debt removal and refactoring actions: Co-occurrence or more?. In: 2019 IEEE international conference on software maintenance and evolution (ICSME). IEEE, pp 186–190

Karwin B (2010) SQL Antipatterns: Avoiding the pitfalls of database programming Pragmatic Bookshelf

Khumnin P, Senivongse T (2017) SQL antipatterns detection and database refactoring process. In: 2017 18th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD), pp 199–205

Kim M, Gee M, Loh A, Rachatasumrit N (2010) Ref-finder: a refactoring reconstruction tool based on logic query templates. In: Proceedings of the 18th ACM SIGSOFT international symposium on Foundations of software engineering, pp 371–372

Kurtanović Z, Maalej W (2018) On user rationale in software engineering. Requir Eng 23(3):357–379CrossRef

Mahmoudi M, Nadi S, Tsantalis N (2019) Are refactorings to blame? an empirical study of refactorings in merge conflicts. In: 2019 IEEE 26th international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 151–162

McDonald N, Goggins S (2013) Performance and participation in open source software on Github. In: CHI’13 extended abstracts on human factors in computing systems, pp 139–144

Meurice L, Nagy C, Cleve A (2016) Static analysis of dynamic database usage in Java systems. In: International conference on advanced information systems engineering. Springer, pp 491–506

Murphy GC, Kersten M, Findlater L (2006) How are java software developers using the elipse ide? IEEE Softw 23(4):76–83CrossRef

Muse BA, Khomh F, Antoniol G (2022) Do developers refactor data access code? an empirical study. In: the 29th IEEE international conference on software analysis, evolution and reengineering(SANER)

Muse BA, Khomh F, Antoniol G (2022) Replication package: Refactoring Practices in the Context of Data-intensive Systems. https://doi.org/10.5281/zenodo.7140854

Muse BA, Nagy C, Cleve A, Khomh F, Antoniol G (2022) FIXME: synchronize with database! an empirical study of data access self-admitted technical debt. Empir Softw Eng 27(6):130. https://doi.org/10.1007/s10664-022-10119-4CrossRef

Muse BA, Rahman MM, Nagy C, Cleve A, Khomh F, Antoniol G (2020) On the prevalence, impact, and evolution of SQL code smells in data-intensive systems. In: Proceedings of the 17th international conference on mining software repositories, pp 327–338

Nagy C, Cleve A (2017) A static code smell detector for SQL queries embedded in Java code. In: 2017 IEEE 17th international working conference on source code analysis and manipulation (SCAM). IEEE, pp 147–152

Nagy C, Cleve A (2018) SQLInspect: A static analyzer to inspect database usage in Java applications. In: Proceedings of the 40th international conference on software engineering: companion proceedings. ACM, pp 93–96

Park B, Rao DL, Gudivada VN (2021) Dangers of bias in data-intensive information systems. In: Deshpande P, Abraham A, Iyer B, Ma K (eds) Next generation information processing system. Springer Singapore, Singapore, pp 259–271

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine learning in Python. J Mach Learn Res 12:2825–2830MathSciNetMATH

Perez-Palacin D, Merseguer J, Requeno JI, Guerriero M, Di Nitto E, Tamburri DA (2019) A UML profile for the design, quality assessment and deployment of data-intensive applications. Softw Syst Model 18(6):3577–3614CrossRef

Peruma A (2019) A preliminary study of android refactorings. In: 2019 IEEE/ACM 6th international conference on mobile software engineering and systems (MOBILESoft). IEEE, pp 148–149

Peruma A, Mkaouer MW, Decker MJ, Newman CD (2018) An empirical investigation of how and why developers rename identifiers. In: Proceedings of the 2nd international workshop on refactoring, pp 26–33

Peruma A, Mkaouer MW, Decker MJ, Newman CD (2020) Contextualizing rename decisions using refactorings, commit messages, and data types. J Syst Softw 169(110):704

Piatetsky S, Frawley G, William J (1991) Discovery, analysis and presentation of strong rules knowledge discovery in databases

Sharma T, Fragkoulis M, Rizou S, Bruntink M, Spinellis D (2018) Smelly relations: Measuring and understanding database schema quality. In: 2018 IEEE/ACM 40th international conference on software engineering: software engineering in practice track (ICSE-SEIP), pp 55–64

Shome A, Cruz L, van Deursen A (2022) Data smells in public datasets. In: Crnkovic I (ed) Proceedings of the 1st international conference on AI engineering: software engineering for AI, CAIN 2022, Pittsburgh, Pennsylvania, May 16-24, 2022. https://doi.org/10.1145/3522664.3528621. ACM, pp 205–216

Silva D, Silva J, Santos GJDS, Terra R, Valente MTO (2020) Refdiff 2.0: A multi-language refactoring detection tool. IEEE Trans Softw Eng

Silva D, Tsantalis N, Valente MT (2016) Why we refactor? confessions of Github contributors. In: Proceedings of the 2016 24th ACM SIGSOFT international symposium on foundations of software engineering, pp 858–870

Spadini D, Aniche M, Bacchelli A (2018) PyDriller: Python Framework for Mining Software Repositories. https://doi.org/10.1145/3236024.3264598

Tsantalis N, Ketkar A, Dig D (2020) Refactoringminer 2.0. IEEE Transactions on Software Engineering. https://doi.org/10.1109/TSE.2020.3007722

Vassallo C, Grano G, Palomba F, Gall HC, Bacchelli A (2019) A large-scale empirical exploration on refactoring activities in open source software projects. Sci Comput Program 180:1–15CrossRef

Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A (2012) Experimentation in software engineering. Springer Science & Business Media, BerlinCrossRefMATH

Zhou C, Kuttal SK, Ahmed I (2018) What makes a good developer? an empirical study of developers’ technical and social competencies. In: 2018 IEEE symposium on visual languages and human-centric computing (VL/HCC). IEEE, pp 319–321

Zhou J, Wang S, Bezemer CP, Zou Y, Hassan AE (2021) Studying the association between bountysource bounties and the issue-addressing likelihood of Github issue reports. IEEE Trans Softw Eng 47(12):2919–2933. https://doi.org/10.1109/TSE.2020.2974469CrossRef

Title: Refactoring practices in the context of data-intensive systems
Authors: Biruk Asmare Muse
Foutse Khomh
Giuliano Antoniol
Publication date: 01-03-2023
Publisher: Springer US
Published in: Empirical Software Engineering / Issue 2/2023
Print ISSN: 1382-3256
Electronic ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-022-10271-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 2/2023

A multi-model framework for semantically enhancing detection of quality-related bug report descriptions

Calling relationship investigation and application on Ethereum Blockchain System

An empirical assessment of machine learning approaches for triaging reports of static analysis tools

Differential testing for machine learning: an analysis for classification algorithms beyond deep learning

Vulnerability management in Linux distributions

On the assignment of commits to releases

Premium Partner