An Intelligent Early Warning System for Software Quality Improvement and Project Management

One of the main reasons behind unfruitful software development projects is that it is often too late to correct the problems by the time they are detected. It clearly indicates the need for early warning about the potential risks. In this paper, we discuss an intelligent software early warning system based on fuzzy logic using an integrated set of software metrics. It helps to assess risks associated with being behind schedule, over budget, and poor quality in software development and maintenance from multiple perspectives. It handles incomplete, inaccurate, and imprecise information, and resolve conflicts in an uncertain environment in its software risk assessment using fuzzy linguistic variables, fuzzy sets, and fuzzy inference rules. Process, product, and organizational metrics are collected or computed based on solid software models. The intelligent risk assessment process consists of the following steps: fuzzification of software metrics, rule firing, derivation and aggregation of resulted risk fuzzy sets, and defuzzification of linguistic risk variables

Intrusion detection using fuzzy logic and evolutionary algorithm techniques

An Intrusion Detection System should optimally be capable of detecting both known attacks (misuse detection) and unknown attacks (anomaly detection combined with non-self classification). This thesis research studies the problem of automating the generation of a high-fidelity ‘detection model’ that can recognize both known and variations on known attacks through the use of a Fuzzy Learning Classifier System. Experimental results on the classic KDDCup’99 benchmark dataset reveal that the proposed model outperforms published results obtained with the well-known C4.5 classification program. Fuzzy Logic and Evolutionary Computation are very robust in modeling real-world problems like intrusion detection. Therefore, the proposed model is aimed at using fuzzy rules for effective intrusion detection with the goal of evolving the rules over time with a Learning Classifier System. This approach is complemented with the optimization of the membership functions for the fuzzy rules using Evolutionary Algorithms. This hybrid approach was shown to significantly improve the accuracy of an Intrusion Detection System --Abstract, page iii

An Intelligent Early Warning System for Software Quality Improvement and Project Management

One of the main reasons behind unfruitful software development projects is that it is often too late to correctthe problems by the time they are detected. It clearlyindicates the need for early warning about the potentialrisks. In this paper, we discuss an intelligent softwareearly warning system based on fuzzy logic using an integrated set of software metrics. It helps to assess risks associated with being behind schedule, over budget, andpoor quality in software development and maintenancefrom multiple perspectives. It handles incomplete,inaccurate, and imprecise information, and resolveconflicts in an uncertain environment in its software riskassessment using fuzzy linguistic variables, fuzzy sets, andfuzzy inference rules. Process, product, and organizational metrics are collected or computed based on solid software models. The intelligent risk assessment process consists of the following steps: fuzzification of software metrics, rule firing, derivation and aggregation of resulted risk fuzzy sets, and defuzzification of linguistic risk variables