Dilip Kumar Yadav

A fuzzy logic based approach for phase-wise software defects prediction using software metrics

Display Omitted We present a fuzzy logic based approach for phase-wise software defects prediction.Top-most reliability relevant software metrics of SDLC are considered.The proposed model is validated on 20 real software projects.The sensitivity analysis of software metrics is presented.It is useful to analyze the defects severity in different artifacts of SDLC. ContextThe software defect prediction during software development has recently attracted the attention of many researchers. The software defect density indicator prediction in each phase of software development life cycle (SDLC) is desirable for developing a reliable software product. Software defect prediction at the end of testing phase may not be more beneficial because the changes need to be performed in the previous phases of SDLC may require huge amount of money and effort to be spent in order to achieve target software quality. Therefore, phase-wise software defect density indicator prediction model is of great importance. ObjectiveIn this paper, a fuzzy logic based phase-wise software defect prediction model is proposed using the top most reliability relevant metrics of the each phase of the SDLC. MethodIn the proposed model, defect density indicator in requirement analysis, design, coding and testing phase is predicted using nine software metrics of these four phases. The defect density indicator metric predicted at the end of the each phase is also taken as an input to the next phase. Software metrics are assessed in linguistic terms and fuzzy inference system has been employed to develop the model. ResultsThe predictive accuracy of the proposed model is validated using twenty real software project data. Validation results are satisfactory. Measures based on the mean magnitude of relative error and balanced mean magnitude of relative error decrease significantly as the software project size increases. ConclusionIn this paper, a fuzzy logic based model is proposed for predicting software defect density indicator at each phase of the SDLC. The predicted defects of twenty different software projects are found very near to the actual defects detected during testing. The predicted defect density indicators are very helpful to analyze the defect severity in different artifacts of SDLC of a software project.

Early software reliability analysis using reliability relevant software metrics

The early software reliability analysis is very useful for improving the quality of software at reduced testing effort. Software defect density indicator predicted in the early phases (requirement analysis, design and coding phases) provides an opportunity for the early identification of cost overrun, software development process issues and optimal development strategies. Failure data is not available in the early phases of the software development life cycle (SDLC). However, qualitative values of software metrics are available in the early phases of SDLC. Therefore, in this paper, a model is proposed to predict the software defect density indicator of early phases of SDLC using fuzzy logic and the reliability relevant software metrics of early artifacts. The proposed model is applied on twenty real software projects. It is observed that the requirement analysis phase defect density indicator value is relatively greater than that of the design and coding artifacts. The model is validated with the existing literature. Validation result is satisfactory.

A Multistage Model for Defect Prediction of Software Development Life Cycle Using Fuzzy Logic

In this paper, a multistage model for software defect density indicator is proposed using the top most reliability relevant metrics and Fuzzy Inference System (FIS). Prediction of defect in each phase of software development life cycle (SDLC) is desirable for effective decision-support and trade-off analysis during early development phases. The predictive accuracy of proposed model is validated using nine real software projects data. Validation results are satisfactory.

Software defects estimation using metrics of early phases of software development life cycle

An estimation of software defects can be obtained in the later phase of software testing. However, with the aim of cost-effectiveness and timely management of resources, the software defects estimation in the early phases of software development life cycle (SDLC) is one of the major research areas. In this paper, a software defect estimation model is proposed using Bayesian belief network (BBN) and reliability relevant metrics of early phases of SDLC (e.g., requirement analysis, design and coding phases). The causal relationship of software metrics is modeled using BBN. The qualitative value of software metrics and expert assessment of software defects is used for developing the proposed model. The defects estimation accuracy of the proposed model is examined using qualitative data set of ten real software projects. The defects estimation results are compared with the existing model and found more accurate.

A Fuzzy Logic Approach for Multistage Defects Density Analysis of Software

The prediction of software defects in a software project has recently attracted the attention of many researchers. Prediction of defect density indicator (DDI) in each phase of software development life cycle (SDLC) is desirable for effective decision support and trade-off analysis during software development, and also, it improves the reliability of software project and helps software manager to achieve reliable software product within time and costs. The reliability-relevant software metrics impose major impact on the quality of software project at each software development stage. However, software metrics are associated with uncertainty and can be assessed in linguistic terms. Therefore, in this paper, a multistage model for software DDI is proposed using the topmost reliability-relevant metrics and fuzzy inference system (FIS).The predictive accuracy of proposed model is validated using real software projects data. Validation results are satisfactory.

A method for generating membership function from numerical data

Construction of membership functions from numerical data is very important in various applications of the fuzzy set theory. In last two decade, many methods of membership function generation have been developed. Majority of the methods are application domain dependent and complex. In this paper, a simple method for the construction of membership functions from numerical data is proposed. To validate the proposed method, commonly used and suggested evaluation measures: average error rate, mean magnitude of relative error (MMRE), balanced mean magnitude of relative error (BMMRE), and coefficient of determination (R 2 ), have been taken. The validating results show that proposed method has a higher accuracy than existing methods. The sensitivity analysis has been performed to analyze the impact of input variable on the output variable.

Forecasting time-between-failures of software using fuzzy time series approach

The problem with many of the software reliability models is that they cannot deal with the situations where the time-between-failures information during software testing phase is associated with uncertainty due to fuzzy nature of software testing and debugging. Therefore, in this paper, a procedure is proposed to forecast times-between-failures of software during its testing phase by employing fuzzy time series approach, where time-between-failures of software is represented by a fuzzy set having trapezoidal membership function. The forecasting capability of the proposed approach is examined using software failure data of two real software development projects.

Network Security Using ECC with Biometric

The popular asymmetric cryptography is RSA but most of the RSA–based hardware and software products and standards require big cryptographic keys length for higher security level. The existing asymmetric cryptography algorithms need the storage of the secret keys. Stored keys are often protected by poorly selected user passwords that can either be guessed or obtained through brute force attacks. This is a major weakness of the crypto-system. Combining biometrics with cryptography is seen as a possible solution. This paper discusses the network security using Elliptic Curve Cryptography with contactless palm vein biometric system. It provides more security with less key length and also there is no need to store any private key anywhere. It focuses to create and share secret key without transmitting any private key so that no one could access the secret key except themselves.

A method for developing node probability table using qualitative value of software metrics

Recently, Bayesian Belief Network (BBN) becomes one of the most popular choices for uncertainty modeling and has been widely used in software engineering such as defect prediction, reliability and quality prediction, testing effort prediction and software risk assessment. The Node Probability Tables (NPT) play a vital role in BBN. Failure data is not available in the early phases (i.e., phases which occur before testing phase) of software development life cycle (SDLC). However, metrics of early phases of SDLC can be assessed qualitatively. Therefore, an intelligent selection of software metrics also plays a vital role in developing a software defect prediction model using BBN. In this paper, a technique has been proposed to develop the NPT of a BBN using the qualitative value of software metrics.

A Novel Approach to Text Steganography Using Font Size of Invisible Space Characters in Microsoft Word Document

Steganography is the hidden way of communication, where one individual communicates with another through cover medium without giving any doubt about the secret communication to the intermediary. In this paper, we propose a novel approach to text steganography in Microsoft Word document. The idea behind this technique is that slight variation in font size of invisible character space from other characters is not reflected in the document and in the required disk size for the document. Thus, steganography can be intelligently achieved. The embedding rate is very high in this technique, which increases with the increase in blank space character between words. The secret data hiding and revealing technique is presented.