A Fuzzified Approach for the Prediction of Fault Proneness and Defect Density
نویسنده
چکیده
The requirement to improve software productivity has promoted the research on software metrics technology. Object Oriented paradigm is the technology being used to build fault free and stupendous softwares; and to make them fault free object oriented metrics are being used. These metrics are used to identify high risk components early in the design phase and hence help us to reduce the rework and improve the software productivity. CK metrics can be used to obtain the fault proneness and MOOD metrics can be used to obtain the defect density in the modules. An algorithm using fuzzy logic toolbox has been proposed to measure fault proneness and defect density, and the results are shown. This process can be used to discover faults and defects in the early phases of the software development process and hence can be used to minimize rework. Index Terms – CK Metrics suite, Fuzzy inference system, Mamdani inference model, MOOD Metrics, Sugeno inference model.
منابع مشابه
Evaluation of Classifiers in Software Fault-Proneness Prediction
Reliability of software counts on its fault-prone modules. This means that the less software consists of fault-prone units the more we may trust it. Therefore, if we are able to predict the number of fault-prone modules of software, it will be possible to judge the software reliability. In predicting software fault-prone modules, one of the contributing features is software metric by which one ...
متن کاملAn Approach to Early Fault Prediction in Software Systems Using K- Means Clustering
Quality of a software component can be measured in terms of fault proneness of data. Quality estimations are made using fault proneness data available from previously developed similar type of projects and the training data consisting of software measurements. To predict faulty modules in software data different techniques have been proposed which includes statistical method, machine learning m...
متن کاملModeling Of Fault Prediction Using Machine Learning Techniques
Predicting faults early in the software life cycle can be used to improve software process control and achieve high software reliability. Quality of software is increasingly important and testing related issues are becoming crucial for software. Methodologies and techniques for predicting the testing effort, monitoring process costs, and measuring results can help in increasing efficiency of so...
متن کاملA Statistical Framework for the Prediction of Fault-Proneness
Accurate prediction of fault prone modules in software development process enables effective discovery and identification of the defects. Such prediction models are especially valuable for the large-scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. This paper presents a methodology for predicting fault prone m...
متن کاملA Subtractive Clustering Based Approach for Early Prediction of Fault Proneness in Software Modules
In this paper, subtractive clustering based fuzzy inference system approach is used for early detection of faults in the function oriented software systems. This approach has been tested with real time defect datasets of NASA software projects named as PC1 and CM1. Both the code based model and joined model (combination of the requirement and code based metrics) of the datasets are used for tra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009