Using the Case-Based Ranking Methodology for Test Case Prioritization

Abstract

The test case execution order affects the time at which the objectives of testing are met. If the objective is fault detection, an inappropriate execution order might reveal most faults late, thus delaying the bug fixing activity and eventually the delivery of the software. Prioritizing the test cases so as to optimize the achievement of the testing goal has potentially a positive impact on the testing costs, especially when the test execution time is long.Test engineers often possess relevant knowledge about the relative priority of the test cases. However, this knowledge can be hardly expressed in the form of a global ranking or scoring. In this paper, we propose a test case prioritization technique that takes advantage of user knowledge through a machine learning algorithm, Case-Based Ranking (CBR). CBR elicits just relative priority information from the user, in the form of pairwise test case comparisons. User input is integrated with multiple prioritization indexes, in an iterative process that successively refines the test case ordering. Preliminary results on a case study indicate that CBR overcomes previous approaches and, for moderate suite size, gets very close to the optimal solution.

Enhancement in K-mean Clustering to Analyze Software Architecture using Normalization

“…In this paper the major impact of this study shown that architecture recovery can be done batter by fuzzy clustering instead of ordinary clustering. In this paper [2] they explained the adaptive fuzzy algorithm which is come along with the capability and adaptation. This adaptive caliber can be fulfill by using the tool of partition and consolidate it.…”

Test Case Prioritization Based on Analysis of Program Structure

“…Only a few proposed techniques [17,18] aim to improve the faults detection rate for not only the regression testing but also non-regression testing. In [18] Tonella et al treat the test case prioritization problem as a machine learning problem.…”

“…In [18] Tonella et al treat the test case prioritization problem as a machine learning problem. The authors propose a prioritization technique based on machine learning algorithm which combines multiple prioritization indexes and utilizes information extracted from user knowledge to prioritize test cases.…”

“…Therefore, they are hard to be applied to non-regression testing where neither previous execution data of test cases nor code difference information is available. Although some techniques [17,18] attempt to combine multiple information, such as user knowledge and requirements, to prioritize test cases, they fail to consider the fault severity from the viewpoint of program structure. Therefore, the faults with serious threat to system may not be detected early.…”

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

“…Under the umbrella of Human-based approaches Tonella et al suggested case based ranking methodology which is based on machine learning algorithm that uses the user knowledge for prioritization [17]. This approach provides ranking with the help of two inputs-1) a set of initial indicators of priority and 2) pairwise comparisons of test cases obtained from the expert.…”