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Instance Generator and Problem Representation to Improve Object Oriented Code Coverage
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Instance Generator and Problem Representation to Improve Object Oriented Code Coverage

Category : Software Engneering


Sub Category : DOTNET


Project Code : ITDSW01


Project Abstract

INSTANCE GENERATOR AND PROBLEM PRESENTATION

TO IMPROVE OBJECT ORIENTED CODE COVERAGE

ABSTRACT

            This paper based on Search-based approaches has been extensively applied to solve the problem of software test-data generation. Yet, test-data generation for object-oriented programming (OOP) is challenging due to the features of OOP, e.g., abstraction, encapsulation, and visibility that prevent direct access to some parts of the source code. To address this problem we present a new automated search-based software test-data generation approach that achieves high code coverage for unit-class testing. We first describe how we structure the test-data generation problem for unit-class testing to generate relevant sequences of method calls. Through a static analysis, we consider only methods or constructors changing the state of the class-under-test or that may reach a test target. Then we introduce a generator of instances of classes that is based on a family of means-of-instantiation including subclasses and external factory methods. It also uses a seeding strategy and a diversification strategy to increase the likelihood to reach a test target. Using a search heuristic to reach all test targets at the same time, we implement our approach in a tool, JTExpert that we evaluate on more than a hundred Java classes from different open-source libraries. JTExpert gives better results in terms of search time and code coverage than the state of the art, EvoSuite, which uses traditional techniques.

EXISTING SYSTEM

PROPOSED SYSTEM

    EXISTING CONCEPT:-

In Existing approaches the results of this comparison shows that JTExpert is more effective than Evo- Suite because it reaches higher code coverage while it requires less time.

It finds and prepares the different means-of-instantiation existing in the class path. It is responsible for splitting the search space of D1 into where each subspace is represented by a different means-of-instantiation.

 

PROPOSED CONCEPT:-

The proposed approaches have been addressing the problem of automating test-data generation and that fall in Search Based Software Testing (SBST).

The proposed diversification strategy computes a representative complexity measure for each means of instantiation.

    EXISTING ALGORITHM:-

Probabilistic Algorithm

 

PROPOSED ALGORITHM:-

Genetic Algorithm

    ALGORITHM  DEFNITION:-

Our approach differs from previous work in that it provides a formal expressive representation of the test-data generation problem that implicitly reduces the possible number of sequences of method calls. This is also the first approach to provide a probabilistic algorithm to generate a diversified set of needed instances of a class.

ALGORITHM  DEFNITION:-

To improve over random testing, global and local search algorithms have been implemented in several ways. EToc is a pioneering tool that uses genetic algorithms to generate test data that meet some structural criteria. It only deals with primitive types and strings and since its creation in 2004 it has not maintained to better exploit the strengths of recent testing approaches.

    DRAWBACKS:-

Not so efficient if there is heavy interaction between branches

Data should be carefully maintained

ADVANTAGES:-

This technique is very efficient

We can store the data normally and efficiently.


 
 
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