Software engineering activities in the Industry has come a long way with various improvements brought in various stages of the software development life cycle. The complexity of modern software, the commercial constraints and the expectation for high quality products demand the accurate fault prediction based on OO design metrics in the class level in the early stages of software development. The object oriented class metrics are used as quality predictors in the entire OO software development life cycle even when a highly iterative, incremental model or agile software process is employed. Recent research has shown some of the OO design metrics are useful for predicting fault-proneness of classes. In this paper the empirical validation of a set of metrics proposed by Chidamber and Kemerer is performed to assess their ability in predicting the software quality in terms of fault proneness and degradation. We have also proposed the design complexity of object-oriented software with Weighted Methods per Class metric (WMC-CK metric) expressed in terms of Shannon entropy, and error proneness.Property 1: Non-Coarseness Given a class P and a metric µ another class Q can always be found such that: µ(P )_µ(Q). This implies that not every class can have the same value for a metric; otherwise it has lost its value as a measurement.
Property 2: Non-uniqueness (notion of equivalence)There can exist distinct classes P and Q, µ(P ) = µ(Q). This implies that two classes can have the same metric value, i, e., the two classes are equally complex.
Inspection process in software development plays a vital role in effective defect management. In order to have an appropriate measurement of the inspection process, we depend on a process metric called the Depth of Inspection (DI). DI enables the manager within the software community to identify and compare the level of inspection performed in various projects. An empirical study of several projects facilitated the evaluation of a set of process coefficients which are capable of predicting the DI values using multiple regression models. The industry observed DI value based on defect count and the DI value produced by the model are strongly matching. This supports the predictive capability of DI through process coefficients without depending on the prior estimation of the defect count.
Cardiovascular system study using ECG signals have evolved tremendously in the domain of electronics and signal processing. However, there are certain floating challenges unresolved in the analysis and detection of abnormal performances of cardiovascular system. As the medical field is moving towards more automated and intelligent systems, wrong detection or wrong interpretations of ECG waveform of abnormal conditions can be quite fatal. Since the PQRST signals vary their positions randomly, the process of locating, identifying and classifying each feature can be cumbersome and it is prone to errors. Here we present an automated scheme using adaptive wavelet to detect prominent R-peak with extreme accuracy and algorithmically tag and mark the coexisting peaks P, Q, S, and T with almost same accuracy. The adaptive wavelet approach used in this scheme is capable of detecting R-peak in ECG with 99.99% accuracy along with the rest of the waveforms.
In this paper an effective estimation model is proposed for software reusability. The structural properties of the software are analyzed at design level with an engineering approach. This leads to the analysis of intricate relationship existing between the reusability and the design properties. An estimation model is created based on the empirical studies and weighted combination of polynomials using CK meas-ures.
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