Towards a structural load testing tool

Yang, Cheer-Sun; Pollock, Lori

doi:10.1145/229000.226318

Cited by 8 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Load testing is conducted on a system (either a prototype or a fully functional system) rather than on a design or an architectural model. In the case Load Testing [14], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], Stress Testing [18], [26], [30], [31], [32], [33], [34] Yes Detecting violations in performance requirements under load Testing (After Conventional Functional Testing)…”

Section: Load Testingmentioning

confidence: 99%

“…Load sensitive regions are code segments, whose correctness depends on the amount of input data and the duration of testing [18]. Examples of load sensitive regions can be code dealing with various types of resource accesses (e.g., memory, thread pools and database accesses).…”

Section: ) Testing Loads Derived Using Data Flow Analysismentioning

confidence: 99%

“…Examples of load sensitive regions can be code dealing with various types of resource accesses (e.g., memory, thread pools and database accesses). Yang et al [18] use data flow analysis of the system's source code to generate loads, which exercise the load sensitive regions. Their technique detects memory related faults (e.g., memory allocation, memory deallocation and pointers referencing).…”

Section: ) Testing Loads Derived Using Data Flow Analysismentioning

confidence: 99%

“…Rather than matching the code for specific patterns (e.g., the resource accesses patterns in [18]), Zhang et al [29], [111] …”

Section: ) Testing Loads Derived Using Symbolic Executionsmentioning

confidence: 99%

“…There are many different interpretations of load testing, both in the context of academic research and industrial practices (e.g., [10], [11], [12], [13]). In addition, the term load testing is often used interchangeably with two other terms: performance testing (e.g., [14], [15], [16]) and stress testing (e.g., [11], [17], [18]). In this section, we first provide our "own" working definition of load testing by contrasting among various interpretations of load, performance and stress testing.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Survey on Load Testing of Large-Scale Software Systems

Jiang

Hassan²

2015

IIEEE Trans. Software Eng.

139

View full text Add to dashboard Cite

Many large-scale software systems must service thousands or millions of concurrent requests. These systems must be load tested to ensure that they can function correctly under load (i.e., the rate of the incoming requests). In this paper, we survey the state of load testing research and practice. We compare and contrast current techniques that are used in the three phases of a load test: (1) designing a proper load, (2) executing a load test, and (3) analyzing the results of a load test. This survey will be useful for load testing practitioners and software engineering researchers with interest in the load testing of large-scale software systems.

show abstract

Section: Load Testingmentioning

confidence: 99%

Section: ) Testing Loads Derived Using Data Flow Analysismentioning

confidence: 99%

Section: ) Testing Loads Derived Using Data Flow Analysismentioning

confidence: 99%

“…Rather than matching the code for specific patterns (e.g., the resource accesses patterns in [18]), Zhang et al [29], [111] …”

Section: ) Testing Loads Derived Using Symbolic Executionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Load Testing of Large-Scale Software Systems

Jiang

Hassan²

2015

IIEEE Trans. Software Eng.

139

View full text Add to dashboard Cite

show abstract

An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning

et al. 2021

View full text Add to dashboard Cite

Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case-based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learned by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learned policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments in a simulated performance testing setup, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process and performs adaptively without access to source code and performance models.

show abstract

Automatic generation of load tests

Zhang

Elbaum

Dwyer

2011

2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011)

View full text Add to dashboard Cite

Load tests aim to validate whether system performance is acceptable under peak conditions. Existing test generation techniques induce load by increasing the size or rate of the input. Ignoring the particular input values, however, may lead to test suites that grossly mischaracterize a system's performance. To address this limitation we introduce a mixed symbolic execution based approach that is unique in how it 1) favors program paths associated with a performance measure of interest, 2) operates in an iterative-deepening beam-search fashion to discard paths that are unlikely to lead to high-load tests, and 3) generates a test suite of a given size and level of diversity. An assessment of the approach shows it generates test suites that induce program response times and memory consumption several times worse than the compared alternatives, it scales to large and complex inputs, and it exposes a diversity of resource consuming program behavior.

show abstract

Towards a structural load testing tool

Cited by 8 publications

References 8 publications

A Survey on Load Testing of Large-Scale Software Systems

A Survey on Load Testing of Large-Scale Software Systems

An autonomous performance testing framework using self-adaptive fuzzy reinforcement learning

Automatic generation of load tests

Contact Info

Product

Resources

About