Tools and Techniques Used for Prioritizing Test Cases in Regression Testing

Testing is a very expensive task in term of cost, effort and time and it is necessary step of software development because without testing software cannot be completed. Regression testing is a type of software testing which is widely used in software development and maintenance phase; it also occupies a large portion of the software maintenance budget. There are many software testing tools and Technique that is used to test the software program. This research paper defines some testing approach which reduced person effort and time in regression testing. Software systems are change regularly during development and maintenance face. After software is modified regression testing is applied to software to ensure that it behaves intended and modifications not negatively impacts its original functionality. This paper focus on improving the performance of regression testing by using these approach for regression testing by computing coverage data for evolving software using dataflow analysis and execution tracing

Pair-Wise Time-Aware Test Case Prioritization for Regression Testing

After maintenance, software requires regression testing for its validation. Prioritization of test cases for regression testing is required as software is tested under strict time and other constraints. A Pair-wise time-aware Test Case Prioritization (PTCP) technique has been proposed in this paper that determines the effectiveness of a test case on the basis of total number of faults present in software, number of faults detected till time, and the time of execution of different test cases. It selects that test case which determines maximum new faults, not yet detected, within minimum time. Thus prioritized test suite contains those test cases which are effective and tend to minimize repetitive faults detection. Through two comparative studies, it has been observed that with least wastage of time, the proposed technique performed equally well as other two parallel prioritizing techniques, Average Percentage of Fault Detection (APFD) based prioritization, and Optimal Test Case Prioritization (OTCP)

Industrially Applicable System Regression Test Prioritization in Production Automation

When changes are performed on an automated production system (aPS), new faults can be accidentally introduced in the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and on-site in a very uncomfortable environment. Until now, there is no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results.Comment: 13 pages, https://ieeexplore.ieee.org/abstract/document/8320514

Using test case reduction and prioritization to improve symbolic execution

Scaling symbolic execution to large programs or programs with complex inputs remains difficult due to path explosion and complex constraints, as well as external method calls. Additionally, creating an effective test structure with sym-bolic inputs can be difficult. A popular symbolic execution strategy in practice is to perform symbolic execution not “from scratch ” but based on existing test cases. This paper proposes that the effectiveness of this approach to symbolic execution can be enhanced by (1) reducing the size of seed test cases and (2) prioritizing seed test cases to maximize ex-ploration efficiency. The proposed test case reduction strat-egy is based on a recently introduced generalization of delta-debugging, and our prioritization techniques include novel methods that, for this purpose, can outperform some tradi-tional regression testing algorithms. We show that applying these methods can significantly improve the effectiveness of symbolic execution based on existing test cases

Hybrid and dynamic static criteria models for test case prioritization of web application regression testing

In software testing domain, different techniques and approaches are used to support the process of regression testing in an effective way. The main approaches include test case minimization, test case selection, and test case prioritization. Test case prioritization techniques improve the performance of regression testing by arranging test cases in such a way that maximize fault detection could be achieved in a shorter time. However, the problems for web testing are the timing for executing test cases and the number of fault detected. The aim of this study is to increase the effectiveness of test case prioritization by proposing an approach that could detect faults earlier at a shorter execution time. This research proposed an approach comprising two models: Hybrid Static Criteria Model (HSCM) and Dynamic Weighting Static Criteria Model (DWSCM). Each model applied three criteria: most common HTTP requests in pages, length of HTTP request chains, and dependency of HTTP requests. These criteria are used to prioritize test cases for web application regression testing. The proposed HSCM utilized clustering technique to group test cases. A hybridized technique was proposed to prioritize test cases by relying on assigned test case priorities from the combination of aforementioned criteria. A dynamic weighting scheme of criteria for prioritizing test cases was used to increase fault detection rate. The findings revealed that, the models comprising enhanced of Average Percentage Fault Detection (APFD), yielded the highest APFD of 98% in DWSCM and 87% in HSCM, which have led to improve effectiveness prioritization models. The findings confirmed the ability of the proposed techniques in improving web application regression testing

Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review

Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consuming and resource-intensive. To alleviate that problem, Test case Selection and Prioritization (TSP) techniques have been proposed to improve regression testing by selecting and prioritizing test cases in order to provide early feedback to developers. In recent years, researchers have relied on Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such techniques help combine information about test cases, from partial and imperfect sources, into accurate prediction models. This work conducts a systematic literature review focused on ML-based TSP techniques, aiming to perform an in-depth analysis of the state of the art, thus gaining insights regarding future avenues of research. To that end, we analyze 29 primary studies published from 2006 to 2020, which have been identified through a systematic and documented process. This paper addresses five research questions addressing variations in ML-based TSP techniques and feature sets for training and testing ML models, alternative metrics used for evaluating the techniques, the performance of techniques, and the reproducibility of the published studies

Multifactor Algorithm for Test Case Selection and Ordering

Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls short. The current research is motivated by this concept and proposes a multifactor algorithm incorporated with genetic operators and powerful features. A factor-based prioritizer is introduced for proper handling of tied test cases that emerged while implementing re-ordering. Besides this, a Cost-based Fine Tuner (CFT) is embedded in the study to reveal the stable test cases for processing. The effectiveness of the outcome procured through the proposed minimization approach is anatomized and compared with a specific heuristic method (rule-based) and standard genetic methodology. Intra-validation for the result achieved from the reduction procedure is performed graphically. This study contrasts randomly generated sequences with procured re-ordered test sequence for over '10' benchmark codes for the proposed prioritization scheme. Experimental analysis divulged that the proposed system significantly managed to achieve a reduction of 35-40% in testing effort by identifying and executing stable and coverage efficacious test cases at an earlier phase