Anupama Surendran

Forward slicing algorithm based test data generation

In this paper we have suggested a new method for test data generation using dynamic forward slicing algorithm. Separating the suspicious parts of code from the original program will make the process of test data generation easier. In forward dynamic slicing, the slices produced are much smaller than the original program and the search space for testing is considerably reduced. In this paper, we discuss the forward algorithm and the advantages of using forward dynamic algorithm for test data generation. The test cases are generated by analyzing the constraints present in the slices constructed from forward dynamic slicing algorithm. In the proposed method, the program for which the test data to be generated is analyzed. Forward slicing of the program is done with respect to all the variables whose value is changed during program execution. Slices obtained are then verified for statements which define certain constraints for test data generation. This helps to clearly define the limits of test data and remove the ambiguities about the value of the test data which is to be generated. The test data is then generated based on the constraints. In the proposed method, since forward algorithm is used, the execution traces need not be recorded and analyzing the constraints present in the slices will help in fault localization and debugging. This is one of the most important advantages of our method.

Code clones in program test sequence identification

In this work, we have emphasized the need for software clones in test sequence identification. We have provided some relevant features of code clones which make them apt in assisting test sequence identification. In this paper, we have pointed out the relevance of automated test sequence generation in software development life cycle and we have also presented a classification of clones. We have used program slicing technique to obtain slices of the source program and from these slices software code clones are identified. The main contribution of this work is to highlight the relevance of code clones in test sequence identification and the importance of using program slicing in code clone detection. Applying both static and forward dynamic slicing techniques will make code clone detection more factual. We have also mentioned the need for mapping of values in test sequence identification.

Partial Slices in Program Testing

Program slicing is widely used as an aid in program analysis. In several cases, it is observed that the static slices contain a large number of program statements. Due to this increased size of the static slice, they are of little use in many practical applications. Moreover, the static slices may be less precise compared to dynamic slices. Partial slicing is suggested as a method for program testing in order to eliminate the disadvantages of static slicing. In partial slices, in addition to the static slicing criterion, the user has to provide the program point. Program point specifies the program statement up to which the static slicing is to be performed. The partial slices produced in this manner combines both static and program point information. This in turn is analyzed to verify the constraints and conditions in the slices to remove any obscurity in the program testing process. In this work we propose a partial slicing approach, which can be applied in program testing. An appealing aspect of this work is that, using partial slicing the whole program testing process may be made more direct and effective. Some illustrations which highlight the easiness of the proposed partial slicing are provided to affirm the significance of this work.

Evolution or revolution: the critical need in genetic algorithm based testing

Software testing is one of the most inevitable processes in software development. The field of software testing has seen an extensive use of search based techniques in the last decade. Among the search based techniques, it is the metaheuristic techniques such as genetic algorithm that has garnered the major share of attention from researchers. Looking at the large body of work that has happened and is happening in this field, we feel that it is high time someone studied how well genetic algorithm based techniques fare in practical testing process. Method: In this work, we present a roadmap to the future of genetic algorithm based software testing, based on a review of literature. We have mainly reviewed the works which use genetic algorithm for software test data generation. This independent review is designed to direct the attention of future researchers to the deficiencies of genetic algorithm based testing, their possible solutions and the extent to which they are correctable. The observations from the selected primary studies highlight the issues faced when genetic algorithm is applied in software testing. The observations form the review reveal that the type of genetic algorithm used, fitness function design, population initialization and parameter settings does impact the quality of solution obtained in software testing using genetic algorithm. From the review we conclude that, more generalized approaches can make genetic algorithm based software testing one of the strongest methods in practical software testing. We hope that, this review will be a major breakthrough in genetic algorithm based software testing field.

Extracting Business Rules Using a Partitioned Forward Slicing Approach

Our work deals with the process of extracting rules and logic from business systems during the maintenance phase of software development. The rules and logic thus extracted may be useful in re-engineering, re-use and reverse engineering. We have proposed an approach based on a particular type of program slicing named partitioned forward slicing, which helps to identify the rules and logic spread over the code. The paper not only stresses the need for applying partitioned forward slicing in business applications, but also discusses the relevance of other slicing methods. An appealing aspect of this work is that, the proposed approach simplifies the maintenance phase of commercial software development, making it more direct and effective. This is of great use in commercial applications as maintenance phase is critical to the process. Some samples which highlight the flexibility of the proposed slicing method are also provided to affirm the significance of this work.

Fault localization using forward slicing spectrum

An increase in the cost incurred during manual software testing, debugging process and the requirement for reliable test data have forced the researchers to develop an automated system for software fault localization. We have proposed a forward slicing spectrum for fault localization. The proposed approach alleviates some of the core issues of standalone fault localization techniques such as program slicing and program spectrum based methods.

Slicing based reverse engineering of business applications

Without altering the skeletal framework of the software system, reverse engineering enables an in-depth understanding of the software system by retrieving the most relevant properties of it. This has tremendously increased the need to design effective methods for performing reverse engineering in software system. In this paper, we introduce a forward slicing approach for performing reverse engineering in business applications. The relevance of other slicing methods in reverse engineering is also discussed. An appealing aspect of this work is that, using forward slicing in business applications makes the whole reverse engineering process more direct and effective. Some illustrations which highlight the easiness of the proposed slicing method are also provided to affirm the significance of this work.

Data mining techniques to analyze the risks in stocks/options investment

Data mining is one of the most optimal methods to analyze the data .Nowadays most of the people will be reluctant to invest money in shares and options because of the global economic crisis. In this research paper, a proposal is made in order to analyze the stocks/sectors and options based on various financial parameters using data mining techniques. The neuro-fuzzy logic technique is proposed to use in this proposal, to develop this work and the mining technique is applied to classify the result obtained