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The Internet has facilitated the growth of recommendation system owing to the ease of sharing customer experiences online. It is a challenging task to summarize and streamline the online textual reviews. In this paper, we propose a new framework called Fuzzy based contextual recommendation system. For classification of customer reviews we extract the information from the reviews based on the context given by users. We use text mining techniques to tag the review and extract context. Then we find out the relationship between the contexts from the ontological database. We incorporate fuzzy based semantic analyzer to find the relationship between the review and the context when they are not found therein. The sentence based classification predicts the relevant reviews, whereas the fuzzy based context method predicts the relevant instances among the relevant reviews. Textual analysis is carried out with the combination of association rules and ontology mining. The relationship between review and their context is compared using the semantic analyzer which is based on the fuzzy rules.

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Reliability is one of the quantifiable software quality attributes. Software Reliability Growth Models (SRGMs) are used to assess the reliability achieved at different times of testing. Traditional time-based SRGMs may not be accurate enough in all situations where test effort varies with time. To overcome this lacuna, test effort was used instead of time in SRGMs. In the past, finite test effort functions were proposed, which may not be realistic as, at infinite testing time, test effort will be infinite. Hence in this paper, we propose an infinite test effort function in conjunction with a classical Nonhomogeneous Poisson Process (NHPP) model. We use Artificial Neural Network (ANN) for training the proposed model with software failure data. Here it is possible to get a large set of weights for the same model to describe the past failure data equally well. We use machine learning approach to select the appropriate set of weights for the model which will describe both the past and the future data well. We compare the performance of the proposed model with existing model using practical software failure data sets. The proposed log-power TEF based SRGM describes all types of failure data equally well and also improves the accuracy of parameter estimation more than existing TEF and can be used for software release time determination as well.

Goel proposed generalization of the Goel-Okumoto (G-O) software reliability growth model (SRGM), in order to model the failure intensity function, i.e. the rate of occurrence of failures (ROCOF) that initially increases and then decreases (I/D), which occurs in many projects due to the learning phenomenon of the testing team and a few other causes. The ROCOF of the generalized non-homogenous poisson process (NHPP) model can be expressed in the same mathematical form as that of a two-parameter Weibull function. However, this SRGM is susceptible to wide fluctuations in time between failures and sometimes it seems unable to recognize the I/D pattern of ROCOF present in the datasets and hence does not adequately describe such data. The authors therefore propose a shifted Weibull function ROCOF instead for the generalized NHPP model. This modification to the Goel-generalized NHPP model results in an SRGM that seems to perform better consistently, as confirmed by the goodness of fit statistic and predictive validity metrics, when applied to failure datasets of 11 software projects with widely varying characteristics. A case study on software release time determination using the proposed SRGM is also given.failure intensity function, goodness of fit statistic, mean value function, NHPP model, predictive validity, SRGM,

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