Subburaj Ramasamy ¹, Anuj Singh ¹, Deepak Singal ¹

Affiliations
1 Department of Information Technology, SRM University, Kattankulathur-603203, Tamil Nadu, IN

Abstract

Objectives: A vast multitude of application and systems programming is carried out in C or C++ programming languages. Even in programs written in languages such as Java, C libraries find wide use.Therefore, due to their ubiquitous presence, the security of C and C++ code is of paramount importance. Methods/ Statistical Analysis: A static analysis tool named “TraC++” was developed to detect security vulnerabilities in C and C++ programs. The tool uses a predefined and dynamically updated list of insecure coding constructs to check their presence in a given C/C++ code. Findings: The tool, developed in C#, was found to capture potential security vulnerabilities and insecure coding constructs in a given C/C++ program. A list of vulnerable constructs used in the code along with the line numbers in which they are present are the output provided by the tool. Furthermore, the tool provides suggestions as to how the vulnerable constructs can be replaced with better constructs. Application/Improvement: The tool can find use in static analysis for security violations in programs and libraries developed in the C/C++ programming languages.

Keywords

C/C++, Secure Coding, Security Vulnerabilities, Static Analysis.

Full Text

   

Subburaj Ramasamy ¹, C. A. S. Deiva Preetha ¹

Affiliations
1 School of Computing, SRM University, Kattankulathur, Chennai - 603203, Tamil Nadu, IN

Abstract

Background/Objective: The Software Reliability Engineering discipline is witnessing continued research in the field of Software Reliability Growth Models (SRGMs), in order to develop suitable models so as to match with the phenomenal developments in the software sector. The objective of this paper is to propose a combined dynamically weighed model for reliability analysis. Methods: In general, the ability of a model to describe a dataset adequately is assessed by Goodness of Fit (GoF) measures that a model achieves. We used one of the difficult data sets for evaluation of the GoF performance and the non-linear regression was carried out using Curve fitting application of the MATLABTM software tool. The coefficients of determination R-Square (R2), Sum of Squared Error (SSE), Root Mean Square Error (RMSE) are the different GoF metrics used. Findings: The major challenge for the software projects today, is to measure, analyze and control the level of quality of the delivered software. Undesirably long testing cycles adversely influence time to market and hence software development organizations are very keen on having a good control on the testing cycle. In the last few years many reliability models have been identified and recommended. However, no single model can be considered suitable for all situations. Some models assume the growth of mean value function to be exponential, while other models assume it to follow S-type growth. This poses a challenge to the reliability modeling. The dynamically weighted combination model that we propose in this paper helps to address both the phenomena. The superposition and time transformation property of Non-Homogenous Poisson Process (NHPP) model was considered to derive the proposed model. Application: Using SRGM helps to improve the reliability performance. We have derived a powerful model by combining two well-known models. The proposed model gives excellent GoF performance and provides more confidence both for the customer and for the software development organizations.

Keywords

Dynamically Weighted Combination Models, Goodness of Fit Statistics, Software Reliability Growth Models.

Full Text

   

Subburaj Ramasamy ¹, Indhurani Lakshmanan ¹

Affiliations
1 School of Computing, SRM University, Kattankulathur - 603203, Tamil Nadu, IN

Abstract

Background/Objectives: To design a relatively simple Software Reliability Growth Model (SRGM) with testing effort function using Artificial Neural Network approach. Methods/Statistical Analysis: The results evaluation of the proposed SRGM using Artificial Neural Network (ANN) is measured by calculating the three vital criterians namely; AIC (Akaike Information Criterian), R2 (Coefficient of determination) and RMSE (Root Mean Squared Error). Findings: Traditional time-based models may not be appropriate in some situations where the effort is varying with time. Estimating the total effort required for testing the software in the Software Development Life Cycle (SDLC) is important. Hence, a multi-layer feed-forward Artificial Neural Network (ANN) based SRGM using back propagation training is proposed in this paper by incorporating test effort. The proposed ANN based model provides consistent performance for both exponential and S-shaped growth of mean value functions witnessed in software projects. Application/Improvements: The proposed SRGM using ANN will be performed to be eminently useful for software reliability applications, since it is able to maintain its performance in all situation.

Keywords

Artificial Neural Network, Back Propagation, Software Reliability Growth Model, Software Testing, Testing Effort Estimation.

Full Text

   

Subburaj Ramasamy ¹, A. M. J. Muthu Kumaran ¹

Affiliations
1 School of Computing, SRM University, Kattankulathur 603203, Tamil Nadu, IN

Abstract

Background/Objective: The Software Reliability Growth Models (SRGMs) are mainly used to plan and execute system testing in the Software Development Life Cycle (SDLC). The objective of this research paper is to propose a dynamically weighted fault based combination model for application during this phase of reliability growth testing of software systems. Methods: A dynamically weighted fault based SRGM, which describes equally well the exponential growth and S-shaped growth of mean value function in a software testing process is proposed. Non-linear regression methodology was deployed for parameter estimation of (SRGMs).The curve fitting tool in MATLABTm is used for this purpose. The coefficient of determination R2, Sum of Squared Errors (SSE), Root Mean Squared Error (RMSE) is the goodness of fit measures used to assess the quality of fitting of the SRGMs. Findings: It is found that the proposed combination model describes the failure data better than the constituent models used for combining as revealed by the goodness of fit measures. Applications/ Improvements: The new model can be applied to model reliability growth during testing in software projects with varying characteristics. The model additionally provides vital quality metrics, which can be used to manage the current and future software projects.

Keywords

Dynamically Weighted Combination Model, Exponential Growth, Goodness of Fit, S-shaped Growth, Software Reliability Growth Model.

Full Text