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Prioritizing Program Elements: A Pretesting Effort to Improve Software Quality
Author(s) -
Mitrabinda Ray,
Durga Prasad Mohapatra
Publication year - 2012
Publication title -
isrn software engineering
Language(s) - English
Resource type - Journals
eISSN - 2090-7680
pISSN - 2090-7672
DOI - 10.5402/2012/598150
Subject(s) - computer science , reliability engineering , task (project management) , component (thermodynamics) , prioritization , criticality , source code , regression testing , quality (philosophy) , process (computing) , code (set theory) , measure (data warehouse) , software quality , software , software system , software engineering , software development , data mining , software construction , systems engineering , programming language , set (abstract data type) , process management , engineering , philosophy , physics , epistemology , nuclear physics , thermodynamics
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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