Code-coverage guided prioritized test generation

Li, J. Jenny; Weiss, David M.; Yee, Howell

doi:10.1016/j.infsof.2006.06.007

Cited by 32 publications

(6 citation statements)

References 18 publications

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“…In 2002, some researchers have proposed function level prioritization in addition to statement level prioritization [9]. Li et al suggested a framework that produces the test cases covering the points given higher preference by computing preferences using code coverage capability [10]. Aggrawal et al suggested an approach based on code coverage criteria but this technique especially focuses upon version specific test case prioritization [11].…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique

Chaurasia¹,

Agarwal²

2016

IJDTA

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Section: Related Workmentioning

confidence: 99%

A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique

Chaurasia¹,

Agarwal²

2016

IJDTA

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“…Li et al [48] presented a methodology for code coveragebased path selection and test data generation, based on Li's previous work [47]. They [48] proposed a path selection technique that considers the program priority and call relationships among class methods to identify a set of paths through the code, which has high-priority code unit.…”

Section: Related Workmentioning

confidence: 99%

“…They [48] proposed a path selection technique that considers the program priority and call relationships among class methods to identify a set of paths through the code, which has high-priority code unit. Then, constraint analysis method is used to find object attributes and method parameter values for generating tests to traverse through the selected sequence of paths.…”

Section: Related Workmentioning

confidence: 99%

Prioritizing Program Elements: A Pretesting Effort to Improve Software Quality

Ray

Mohapatra

2012

ISRN Software Engineering

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Improving the efficiency of a testing process is a challenging task. Prior work has shown that often, a small number of bugs account for the majority of the reported software failures; and often, most bugs are found in a small portion of the source code of a program. First, prioritizing the code elements according to their criticality and then conducting testing, will promote to reveal the important bugs at the early phase of testing. Keeping it in view, we propose an efficient test effort prioritization method that give a chance to the tester to focus more on the parts of the source code that are highly influenced towards the system failures or in which, the failures have high impact on the system. We consider five important factors such as influence towards system failures, average execution time, structural complexity, severity, and business value associated with a component and estimates the criticality of the component within a system. We have experimentally proved that our proposed test effort prioritization approach is effective in revealing important bugs at the early phase of testing as it is linked to external measure of defect severity and business value, internal measure of frequency, complexity, and coupling.

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“…Li et al [33] presented a methodology for code coverage-based path selection and test data generation, based on Li's previous work [32] . They [33] proposed a path selection technique that considers the program priority and call relationships among class methods to identify a set of paths through the code, which has high priority code unit.…”

Section: Code Prioritizationmentioning

confidence: 99%

“…They [33] proposed a path selection technique that considers the program priority and call relationships among class methods to identify a set of paths through the code, which has high priority code unit. Then, constraint analysis method is used to find object attributes and method parameter values for generating tests to traverse through the selected sequence of paths.…”

Section: Code Prioritizationmentioning

confidence: 99%

Source Code Prioritization Using Forward Slicing for Exposing Critical Elements in a Program

Ray

Kumawat

Mohapatra

2011

J. Comput. Sci. Technol.

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Even after thorough testing, a few bugs still remain in a program with moderate complexity. These residual bugs are randomly distributed throughout the code. We have noticed that bugs in some parts of a program cause frequent and severe failures compared to those in other parts. Then, it is necessary to take a decision about what to test more and what to test less within the testing budget. It is possible to prioritize the methods and classes of an object-oriented program according to their potential to cause failures. For this, we propose a program metric called influence metric to find the influence of a program element on the source code. First, we represent the source code into an intermediate graph called extended system dependence graph. Then, forward slicing is applied on a node of the graph to get the influence of that node. The influence metric for a method m in a program shows the number of statements of the program which directly or indirectly use the result produced by method m. We compute the influence metric for a class c based on the influence metric of all its methods. As influence metric is computed statically, it does not show the expected behavior of a class at run time. It is already known that faults in highly executed parts tend to more failures. Therefore, we have considered operational profile to find the average execution time of a class in a system. Then, classes are prioritized in the source code based on influence metric and average execution time. The priority of an element indicates the potential of the element to cause failures. Once all program elements have been prioritized, the testing effort can be apportioned so that the elements causing frequent failures will be tested thoroughly. We have conducted experiments for two well-known case studies -Library Management System and Trading Automation System -and successfully identified critical elements in the source code of each case study. We have also conducted experiments to compare our scheme with a related scheme. The experimental studies justify that our approach is more accurate than the existing ones in exposing critical elements at the implementation level.

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Code-coverage guided prioritized test generation

Cited by 32 publications

References 18 publications

A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique

A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique

Prioritizing Program Elements: A Pretesting Effort to Improve Software Quality

Source Code Prioritization Using Forward Slicing for Exposing Critical Elements in a Program

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