Understanding Exception-Related Bugs in Large-Scale Cloud Systems

Chen, Haicheng; Dou, Wensheng; Jiang, Yanyan; Qin, Feng

doi:10.1109/ase.2019.00040

Cited by 28 publications

(11 citation statements)

References 25 publications

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“…In the repository analysis of ECLIPSE and TOM-CAT, we observed only 0.35% and 1.87% of the bugs are related to the exception handling code, respectively. Differently from these findings, Chen et al [30] found about 13% of exception handling bugs in six widely-deployed cloud-based systems (CASSANDRA, HBASE, HDFS, HADOOP MAPREDUCE, YARN, and ZOOKEEPER). Sena et al [19] found an even higher percentage (about 20%) of bugs related to exception handling in popular Java libraries (COMMONS-COLLECTIONS, COMMONS-LANG, LOG4J, MAVEN-FILTERING, PLEXUS-UTILS, EASYMOCK, and SLF4J-API).…”

Section: B Discussion Of Exception Handling Bugsmentioning

confidence: 91%

“…Our categorization of exception handling bugs and their causes has been (partially) used in five different studies [20], [15], [26], [33], [18] The survey we conducted in our article also served as a basis for surveys by de Sousa [13], [34]. Finally, we found 15 papers addressing the five themes for future work that our paper discussed [23], [25], [26], [27], [35], [17], [36], [33], [29], [37], [30], [31], [32], [13], [38].…”

Section: Impactsmentioning

confidence: 95%

“…Three papers and the thesis of Pádua et al [15], [16], [17], [18] cite our article when discussing the importance of exception handling code. Similarly to our article, 19 different papers focus on bugs in the exception handling code [19], [20], [21], [22], [23], [24], [15], [25], [26], [27], [17], [14], [28], [18], [29], [30], [31], [32], [13]. Our categorization of exception handling bugs and their causes has been (partially) used in five different studies [20], [15], [26], [33], [18] The survey we conducted in our article also served as a basis for surveys by de Sousa [13], [34].…”

Section: Impactsmentioning

confidence: 99%

“…Three studies proposed a policy check for exception handling code (without promoting recommendations on the exception handling code) [27], [30], [31]. Filho et al [27] implemented an architectural conformance checking solution to help avoiding exception handling design erosion.…”

Section: A Have Other Papers Followed Up?mentioning

confidence: 99%

“…Their tool provides a declarative language for expressing design constraints regarding exception handling, and a design rule checker to verify the exception handling conformance automatically. Chen et al [30] provided a static analysis tool which automatically detects inaccurate exceptions, i.e., exception handling bugs or anti-patterns, by inspecting the inconsistency between an exception's class and its error message. Kechagia and Spinellis [31] designed a type system to support the identification of bugs that can lead to application crashes due to malformed inputs.…”

Section: A Have Other Papers Followed Up?mentioning

confidence: 99%

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A Reflection on “An Exploratory Study on Exception Handling Bugs in Java Programs”

Ebert

Castor

Serebrenik

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Section: B Discussion Of Exception Handling Bugsmentioning

confidence: 91%

Section: Impactsmentioning

confidence: 95%

Section: Impactsmentioning

confidence: 99%

Section: A Have Other Papers Followed Up?mentioning

confidence: 99%

Section: A Have Other Papers Followed Up?mentioning

confidence: 99%

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A Reflection on “An Exploratory Study on Exception Handling Bugs in Java Programs”

Ebert

Castor

Serebrenik

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Model‐checking‐driven explorative testing of CRDT designs and implementations

Zhang

Huang

Wei

et al. 2023

J Software Evolu Process

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Internet‐scale distributed systems often replicate data at multiple geographic locations to provide low latency and high availability, despite node and network failures. According to the CAP theorem, low latency and high availability can only be achieved at the cost of accepting weak consistency. The conflict‐free replicated data type (CRDT) is a framework that provides a principled approach to maintaining eventual consistency among data replicas. CRDTs have been notoriously difficult to design and implement correctly. Subtle deep bugs lie in the complex and tedious handling of all possible cases of conflicting data updates. We argue that the CRDT design should be formally specified and model checked, to uncover deep bugs which are beyond human reasoning. The implementation further needs to be systematically tested. On the one hand, the testing needs to inherit the exhaustive nature of the model checking and ensures the coverage of testing. On the other hand, the testing is expected to find coding errors which cannot be detected by design level verification. Toward the challenges above, we propose the model‐checking‐driven explorative testing (MET) framework. At the design level, MET uses TLA+ to specify and model check CRDT designs. At the implementation level, MET conducts model‐checking‐driven explorative testing, in the sense that the test cases are automatically generated from the model‐checking traces. The system execution is controlled to proceed deterministically, following the model‐checking trace. The explorative testing systematically controls and permutes all nondeterministic choices of message reorderings. We apply MET in our practical development of CRDTs. The bugs in both designs and implementations of CRDTs are found. As for bugs which can be found by traditional testing techniques, MET greatly reduces the cost of fixing the bugs. Moreover, MET can find subtle deep bugs which cannot be found by existing techniques at a reasonable cost. Based on our practical use of MET, we discuss how MET provides us with sufficient confidence in the correctness of our CRDT designs and implementations.Conflict‐free replicated data type (CRDT) is a framework that provides a principled approach to maintaining eventual consistency among data replicas in distributed systems. CRDTs have been notoriously difficult to design and implement correctly. We propose model‐checking‐driven explorative testing (MET) framework for dealing with such problem. We apply MET in our practical development of CRDTs. MET successfully finds subtle deep bugs and provides us with sufficient confidence in the correctness of our CRDT designs and implementations.

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