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

Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization

SOFTWARE FAULT DETECTION VIA GRAMMAR-BASED TEST CASE GENERATION

Fault detection is helpful to cut down the failure causes by logically locating and eliminating defects. In this thesis, we present a novel fault detection technique via structured input data which can be represented by a grammar. We take a set of well-distributed test cases as input, each of which has a set of test requirements. We illustrate that test requirements come from structured data can be effectively used as coverage criteria to reduce the test suites. We then propose an automatic fault detection approach to locate software bugs which are shown in failed test cases. This method can be applied in testing data-input-critical software such as compilers, translators, reactive systems etc. Preliminary experimental study proves that our fault detection approach is able to precisely locate the faults of software under test from failed test cases

Improving the effectiveness of testing pervasive software via context diversity

Context-aware pervasive software is responsive to various contexts and their changes. A faulty implementation of the context-aware features may lead to unpredictable behavior with adverse effects. In software testing, one of the most important research issues is to determine the sufficiency of a test suite to verify the software under test. Existing adequacy criteria for testing traditional software, however, have not explored the dimension of serial test inputs and have not considered context changes when constructing test suites. In this article, we define the concept of context diversity to capture the extent of context changes in serial inputs and propose three strategies to study how context diversity may improve the effectiveness of the data-flow testing criteria. Our case study shows that the strategy that uses test cases with higher context diversity can significantly improve the effectiveness of existing data-flow testing criteria for context-aware pervasive software. In addition, test suites with higher context diversity are found to execute significantly longer paths, which may provide a clue that reveals why context diversity can contribute to the improvement of effectiveness of test suites. © 2014 ACM.postprin

Trade-off between automated and manual testing: A production possibility curve cost model

Testing is always important for Software Quality Assurance (SQA) activities and key cost multiplier in software development. The decision to automate or not to automate a test case is critical. In this paper we discuss the possibility of test automation and in relation to the trade-off between manual and automated test cases. We purpose a Production cost frontier based technique to distinguish the point of automation and manual test within the cost constraints. Our objective is to identify the facts that up to what extant a testing process can be automated. In this paper a cost model is proposed for deciding the proportion of automated and manual testing. The objective is to find best possible combination of these two and production possibility in one type by eliminating the other type of testin

Selecting Highly Efficient Sets of Subdomains for Mutation Adequacy

Test selection techniques are used to reduce the human effort involved in software testing. Most research focusses on selecting efficient sets of test cases according to various coverage criteria for directed testing. We introduce a new technique to select efficient sets of sub domains from which new test cases can be sampled at random to achieve a high mutation score. We first present a technique for evolving multiple sub domains, each of which target a different group of mutants. The evolved sub domains are shown to achieve an average 160% improvement in mutation score compared to random testing with six real world Java programs. We then present a technique for selecting sets of the evolved sub domains to reduce the human effort involved in evaluating sampled test cases without reducing their fault finding effectiveness. This technique significantly reduces the number of sub domains for four of the six programs with a negligible difference in mutation score

A hybrid weight-based and string distances using particle swarm optimization for prioritizing test cases

Regression testing is concerned with testing the modified version of software. However, to re-test entire test cases require significant cost and time. To reduce the cost and time, higher average percentage fault detection (APFD) rate and faster execution to kill fault mutant are required. Therefore, to achieve these two requirements, an improvement to existing Test Case Prioritization (TCP) technique for a more effective regression testing is offered. A weight-hybrid string distance technique and prioritization using particle swarm optimization (PSO) is proposed. Distance between test cases and weight for each test case, and hybridization of both values for weight-hybrid string distance are calculated. This experiment was evaluated using Siemens dataset. Result obtained from this experiment shows that weight-hybrid string distance is capable of improving APFD values whereby APFD value for hybrid TFIDF-JC is equal to 97.37%, which shows the highest improvement by 4.74% as compared to non-hybrid JC. Meanwhile, for percentage of test cases needed to kill 100% fault mutants, hybrid TFIDF-M yields the lowest value, 22.88%, which shows a 76% improvement as compared to its non-hybrid string distance

Test case prioritization technique based on string distance metrics

Numerous test case prioritization (TCP) approaches have been introduced to enhance the test viability in software testing activity with the goal to maximize early average percentage fault detection (APFD). There are different approaches and the process for each approach varies. Furthermore, these approaches are not well documented within the single TCP approach. Based on current studies, having an approach that has high coverage effectiveness (CE) and APFD rate, remains a challenge in TCP. The string-based approach is known to have a single string distance based metric to differentiate test cases that can improve the CE results. However, to differentiate precisely the test cases, the string distances require enhancement. Therefore, a TCP technique based on string distance metric was developed to improve CE and APFD rate. In this research, to differentiate precisely the test cases and counter the string distances problem, an enhanced string distances based metric with a string weight based metric was introduced. Then, the metric was executed under designed process for string-based approach for complete evaluation. Experimental results showed that the enhanced string metric had the highest APFD with 98.56% and highest CE with 69.82% in Siemen dataset, cstcas. Besides, the technique yielded the highest APFD with 76.38% in Robotic Wheelchair System (RWS) case study. As a conclusion, the enhanced TCP technique with weight based metric has prioritised the test case based on their occurrences which helped to differentiate precisely the test cases, and improved the overall scores of APFD and CE

Model based test suite minimization using metaheuristics

Software testing is one of the most widely used methods for quality assurance and fault detection purposes. However, it is one of the most expensive, tedious and time consuming activities in software development life cycle. Code-based and specification-based testing has been going on for almost four decades. Model-based testing (MBT) is a relatively new approach to software testing where the software models as opposed to other artifacts (i.e. source code) are used as primary source of test cases. Models are simplified representation of a software system and are cheaper to execute than the original or deployed system. The main objective of the research presented in this thesis is the development of a framework for improving the efficiency and effectiveness of test suites generated from UML models. It focuses on three activities: transformation of Activity Diagram (AD) model into Colored Petri Net (CPN) model, generation and evaluation of AD based test suite and optimization of AD based test suite. Unified Modeling Language (UML) is a de facto standard for software system analysis and design. UML models can be categorized into structural and behavioral models. AD is a behavioral type of UML model and since major revision in UML version 2.x it has a new Petri Nets like semantics. It has wide application scope including embedded, workflow and web-service systems. For this reason this thesis concentrates on AD models. Informal semantics of UML generally and AD specially is a major challenge in the development of UML based verification and validation tools. One solution to this challenge is transforming a UML model into an executable formal model. In the thesis, a three step transformation methodology is proposed for resolving ambiguities in an AD model and then transforming it into a CPN representation which is a well known formal language with extensive tool support. Test case generation is one of the most critical and labor intensive activities in testing processes. The flow oriented semantic of AD suits modeling both sequential and concurrent systems. The thesis presented a novel technique to generate test cases from AD using a stochastic algorithm. In order to determine if the generated test suite is adequate, two test suite adequacy analysis techniques based on structural coverage and mutation have been proposed. In terms of structural coverage, two separate coverage criteria are also proposed to evaluate the adequacy of the test suite from both perspectives, sequential and concurrent. Mutation analysis is a fault-based technique to determine if the test suite is adequate for detecting particular types of faults. Four categories of mutation operators are defined to seed specific faults into the mutant model. Another focus of thesis is to improve the test suite efficiency without compromising its effectiveness. One way of achieving this is identifying and removing the redundant test cases. It has been shown that the test suite minimization by removing redundant test cases is a combinatorial optimization problem. An evolutionary computation based test suite minimization technique is developed to address the test suite minimization problem and its performance is empirically compared with other well known heuristic algorithms. Additionally, statistical analysis is performed to characterize the fitness landscape of test suite minimization problems. The proposed test suite minimization solution is extended to include multi-objective minimization. As the redundancy is contextual, different criteria and their combination can significantly change the solution test suite. Therefore, the last part of the thesis describes an investigation into multi-objective test suite minimization and optimization algorithms. The proposed framework is demonstrated and evaluated using prototype tools and case study models. Empirical results have shown that the techniques developed within the framework are effective in model based test suite generation and optimizatio