Software Test Cases Optimization

It is extremely important that the software to be delivered to the user should be reliable and fault free. This
ranges software testing as one of the most important, challenging and dominating activity in the software development life
cycle and hence consequent improvement in its usefulness, both with respect to the time and resources, is taken as a key
factor by many researchers. An imperative measure of the software testing process is running and evaluating test
scenarios. The aim of this part is to assess how well the software under test conforms to its specifications. One of the
conducts to achieve this is to generate the test cases and make use of the test oracle to regulate whether a given test case
exposes a fault. This procedure incorporates a lot of time. It is very difficult to develop correct, good and unique test cases
manually. Use of an automated oracle can achieve the reduction in software testing time and hence the reduction of the
cost of the testing process. The use of the Meta Heuristic algorithms has been proposed as a tool for software testing.
Optimization is a ubiquitous and spontaneous process which is an integral part of our daily life. Meta Heuristic
Algorithms are widely used to tackle black box global optimization problems when no prior knowledge is available.
Differential Evolution (DE) is a well-known evolutionary algorithm for solving global optimization problems. Its
performance is quite dependent on setting of its control parameters. DE has mainly three control parameters namely,
mutation factor (F), crossover rate (CR) and population size (NP). Mutation operator plays a very important role in any
evolutionary algorithm. This operator helps to increase search diversity and avoids stagnation in local optima. The main
contribution of this thesis report is automatic test oracle design using Neural Network. It uses an extension of differential
evolution, called as Modified Differential Evolution (MDE) for test data generation. The performance of Differential
Evolution has been improved by introducing a new mutation operator in MDE so that its convergent rate and robustness
can be improved. This MDE is applied for solving Travelling Salesman Problem (TSP). The results obtained are fed as an
input to train the neural network and then this trained neural network behaves as an automated test oracle. Experimental
results show that proposed (MDE) is better and comparable to the other variants of differential evolution. The
performance of MDE is analyzed on 30 benchmark functions for four different dimensions viz. 10, 30, 50,100 described in
Congress on Evolutionary Computing -2014.

The overall scenario of the software testing is to confirm delivery of quality product to the end user. It is required for high quality software that possibilities of errors and bugs occurrence in production are very less. Development of software contains complexity and errors which occur at any stage. Sometimes these errors or bugs could be effect in terms of losses respect to time and money if not identified and solved as early as possible. Software Testing ensures that software meets user specifications and requirements which works as expected as it should be. In any case, the field of software testing has various issues like efficacious generation of test data or test cases, prioritization of test data and so on which need be handled. Testing also include as a major part in Software development life cycle. Effective and Efficient manner in testing can help to fix the issues in time and make software stable.

One of the key challenges in software testing today is prioritizing and evaluating test cases. The decision of which test cases to design, select and execute first is of great importance, in particular considering that testing is often done late in the implementation process, and therefore needs to be done within tight resource constraints on time and budget. In practice, prioritized selection of test cases requires the evaluation of different test case criteria, and therefore, test case prioritization can be formulated as a multi-criteria decision making problem. As the number of decision criteria grows, application of a systematic decision making solution becomes a necessity. In this paper we propose an approach for prioritized selection of test cases by using the Analytic Hierarchy Process (AHP) technique. To improve the practicality of the approach in real world scenarios, we apply AHP in fuzzy environment so that criteria values can be specified using fuzzy variables than requiring precise quantified values. One of the advantages of the decision making process it that the defined criteria with the biggest and most critical role in priority ranking of test cases is identified. We have applied our approach on an example case in which several test cases for testing non-functional requirements in a systems are defined.

In industrial software testing, development projects typically set up and maintain test suites containing large numbers of test cases. Executing a large number of test cases can be expensive in terms of effort and wall-clock time. Moreover, indiscriminate execution of all available test cases typically lead to sub-optimal use of testing resources. On the other hand, selecting too few test cases for execution might leave a large number of faults undiscovered. Limiting factors such as allocated budget and time constraints for testing further emphasizes the importance of test case prioritization in order to identify test cases that enable earlier detection of faults while respecting such constraints. In this paper, we propose a multi-criteria decision making approach for prioritizing test cases in order to detect faults earlier. This is achieved by applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision making technique combined with fuzzy principles. Our solution is based on important criteria such as fault detection probability, execution time, complexity, and other test case properties. By applying the approach on a train control management subsystem from Bombardier Transportation in Sweden, we demonstrate how it helps, in a systematic way, to identify test cases that can lead to early detection of faults while respecting various criteria.