Refactoring Object-Relational Database Applications by Applying Transformation Rules to Develop better Performance

Davar, Zahra; Handoko,

doi:10.1145/2684200.2684304

Cited by 1 publication

(2 citation statements)

References 15 publications

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“…Furthermore, their technique provides an assessment mechanism for prioritizing the fixes of the detected performance issues. Subsequent work [28], [29] focused on fixing performance issues through automated means. In particular, Singh et al [29] introduced a genetic algorithm for tuning the configuration of ORM systems to achieve better performance.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, Singh et al [29] introduced a genetic algorithm for tuning the configuration of ORM systems to achieve better performance. Davar et al [28] proposed a refactoring framework by applying a set of known transformation rules to inefficient ORM-based code. Unlike prior work that finds issues in the ORM-based applications, our work is the first to find issues in the ORM implementations.…”

Section: Related Workmentioning

confidence: 99%

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Data-Oriented Differential Testing of Object-Relational Mapping Systems

Sotiropoulos

Chaliasos

Atlidakis

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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We introduce, what is to the best of our knowledge, the first approach for systematically testing Object-Relational Mapping (ORM) systems. Our approach leverages differential testing to establish a test oracle for ORM-specific bugs. Specifically, we first generate random relational database schemas, set up the respective databases, and then, we query these databases using the APIs of the ORM systems under test. To tackle the challenge that ORMs lack a common input language, we generate queries written in an abstract query language. These abstract queries are translated into concrete, executable ORM queries, which are ultimately used to differentially test the correctness of target implementations. The effectiveness of our method heavily relies on the data inserted to the underlying databases. Therefore, we employ a solver-based approach for producing targeted database records with respect to the constraints of the generated queries. We implement our approach as a tool, called CYNTHIA, which found 28 bugs in five popular ORM systems. The vast majority of these bugs are confirmed (25 / 28), more than half were fixed (20 / 28), and three were marked as release blockers by the corresponding developers.Index Terms-Object-Relational Mapping, Differential Testing, Automated Testing 1 from django.db import models 2 class Person(models.Model): 3 age = models.IntegerField() 4 name = models.CharField(max_length=20) 5 ... 6 p1 = Person(age=31, name="John") 7 p1.save() 8 p2 = Person.objects.get(age=32) 9 p2.delete() 1 expr = (1 + T.col) 2 squared = (expr * expr) 3 T.select(fn.sum(expr), fn.avg(squared)).all() 4 // Generated SQL

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%