Data-Oriented Differential Testing of Object-Relational Mapping Systems

Sotiropoulos, Thodoris; Chaliasos, Stefanos; Atlidakis, Vaggelis; Mitropoulos, Dimitris; Spinellis, Diomidis

doi:10.1109/icse43902.2021.00137

Cited by 20 publications

(5 citation statements)

References 30 publications

(54 reference statements)

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“…Code clone, or repetitive code, is a code smell that can arise when a developer copies and pastes a piece of code from one place to another [27]. This code clone method may lead to software quality problems [28]- [30], and there are some works in previous research committed to detect and solve these odors through various methods [5], [6], [16], [31].…”

Section: B Code Smells and Anti-patternsmentioning

confidence: 99%

Mining the Relationship between Object-Relational Mapping Performance Anti-patterns and Code Clones

Xu,

Zhu,

Yang

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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The use of Object-Relational Mapping (ORM) in software development has become increasingly popular due to its superiority to simplify database interactions. Despite the prosperous development of ORM code smells detection tools for general code smell problems related to coupling and cohesion, these tools do not capture issues that are specific to ORM code statement context. In this work, we fill the gap wit h the potential performance anti-patterns of repetitive ORM code by heuristic analysis and code clone analysis on 6 open source ORM systems in Java and Python (Saler, Wagtail, Zulip, Taiga, Protal, and Roller). For each occurrence of this code smell, we distinguished problematic instances that potentially require further fixes among justifiable ones. Through our research, we identified four antipatterns associated with repetitive ORM code and proposed fix strategy for each of these anti-patterns. Additionally, our study delves into the relationship between repetitive ORM code anti-patterns and code clone, which reveals that a substantial proportion of repetitive ORM statements can be found in cloned code. Experiments show that repetitive ORM code can lead to a waste of system performance. This research highlights the impact of ORM code context on the proper use of ORM frameworks and emphasizes that copying ORM code without context evaluation can be detrimental to system performance.

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Section: B Code Smells and Anti-patternsmentioning

confidence: 99%

Mining the Relationship between Object-Relational Mapping Performance Anti-patterns and Code Clones

Xu,

Zhu,

Yang

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…TVMfuzz [29] conducts mutationbased fuzzing to test deep learning compilers with some novel mutation operators to facilitate type-related bug detection. CYN-THIA [33] detects bugs in object-relational mapping (ORM) implementations by employing a solver-based approach for generating targeted database records with respect to the constraints of the generated queries. To detect bugs in Datalog engines, queryFuzz [14] uses metamorphic transformations based on database theory and performs metamorphic testing.…”

Section: Related Workmentioning

confidence: 99%

GDsmith: Detecting Bugs in Graph Database Engines

Lin¹,

Hua²,

Ren³

et al. 2022

Preprint

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Graph database engines stand out in the era of big data for their efficiency of modeling and processing linked data. There is a strong need of testing graph database engines. However, random testing, the most practical way of automated test generation, faces the challenges of semantic validity, non-empty result, and behavior diversity to detect bugs in graph database engines. To address these challenges, in this paper, we propose GDsmith, the first black-box approach for testing graph database engines. It ensures that each randomly generated Cypher query satisfies the semantic requirements via skeleton generation and completion. GDsmith includes our technique to increase the probability of producing Cypher queries that return non-empty results by leveraging three types of structural mutation strategies. GDsmith also includes our technique to improve the behavior diversity of the generated Cypher queries by selecting property keys according to their previous frequencies when generating new queries. Our evaluation results demonstrate that GDsmith is effective and efficient for automated query generation and substantially outperforms the baseline. GDsmith successfully detects 27 previously unknown bugs on the released versions of three popular open-source graph database engines and receive positive feedback from their developers. CCS CONCEPTS• Software and its engineering → Software testing and debugging; • Information systems → Database query processing; • Security and privacy → Database and storage security.

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“…The steady state in this scenario is defined such that the SQL execution outputs are consistent between two databases. Knowledgeable audiences may notice that this setup expands the standard "differential testing" procedure [29], [30], [31], in which the experimental group must act consistently with the reference even while the former group is under the stress of CE. ② Picking Chaos Variables.…”

Section: Chaos Engineering (Ce)mentioning

confidence: 99%

PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

Ji¹,

Ma²,

Wang³

2022

Preprint

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Debugging performance anomalies in real-world databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrade. Nevertheless, causality analysis is practically challenging, particularly due to limited observability. Recently, chaos engineering has been applied to test complex real-world software systems. Chaos frameworks like Chaos Mesh mutate a set of chaos variables to inject catastrophic events (e.g., network slowdowns) to "stress" software systems. The systems under chaos stress are then tested using methods like differential testing to check if they retain their normal functionality (e.g., SQL query output is always correct under stress). Despite its ubiquity in the industry, chaos engineering is now employed mostly to aid software testing rather for performance debugging. This paper identifies novel usage of chaos engineering on helping developers diagnose performance anomalies in databases. Our presented framework, PERFCE, comprises an offline phase and an online phase. The offline phase learns the statistical models of the target database system, whilst the online phase diagnoses the root cause of monitored performance anomalies on the fly. During the offline phase, PERFCE leverages both passive observations and proactive chaos experiments to constitute accurate causal graphs and structural equation models (SEMs). When observing performance anomalies during the online phase, causal graphs enable qualitative root cause identification (e.g., high CPU usage) and SEMs enable quantitative counterfactual analysis (e.g., determining "when CPU usage is reduced to 45%, performance returns to normal"). PERFCE notably outperforms prior works on common synthetic datasets, and our evaluation on real-world databases, MySQL and TiDB, shows that PERFCE is highly accurate and moderately expensive.

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Data-Oriented Differential Testing of Object-Relational Mapping Systems

Cited by 20 publications

References 30 publications

Mining the Relationship between Object-Relational Mapping Performance Anti-patterns and Code Clones

Mining the Relationship between Object-Relational Mapping Performance Anti-patterns and Code Clones

GDsmith: Detecting Bugs in Graph Database Engines

PerfCE: Performance Debugging on Databases with Chaos Engineering-Enhanced Causality Analysis

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