Prabhat Ranjan ¹, Sushant Kumar ¹, Uttam Kumar ²

Affiliations
1 Department of Computer Science, Central University of South Bihar, BIT Campus, Patna – 800014, Bihar, IN
2 Department of Computer Science, Central University of South Bihar, BIT Campus, Patna – 800014, Bihar

Abstract

Objectives: Software fault prediction is a vital activity in software development to make software more efficient, economic and produce quality software. This is a fruitful approach to decreases the testing efforts (cost and time) of the software testing. Methods: Existence of faults not only reduces the standard of the software, but also increases its development cost and time. In this paper, the detailed survey has done on software fault prediction for categorize software modules as faulty or non-faulty. The goal of this paper is to find the techniques that has used in software fault prediction. The computational intelligence techniques like soft computing, data mining and machine learning based software fault prediction approaches have included. The classification accuracy rate of different methods which is used for fault prediction has explored. Finding: From the survey, it is found that the fuzzy logic and McCabe based metrics are better way to handle the software fault prediction. The McCabe metrics has independent from the programming language. It performs good result for different size of the software. Application: This survey will helpful to select the model which have high accuracy rate for early phase software testing to provide the quality software. The survey also helps to reduce the cost and time of software testing.

Keywords

Fuzzy Logic, Software Testing, Software Fault Prediction, Software Fault Detection, Soft Computing

Full Text

   

Shubhodip Saha ¹, Jainath Yadav ¹, Prabhat Ranjan ¹

Affiliations
1 Department of Computer Science, Central University of South Bihar, Patna – 800014, Bihar, IN

Abstract

Background: Sarcasm detection in twitter is a very important task as it had helped in the analysis of tweets. With the help of sarcasm detection, companies could analyze the feelings of user about their products. This is helpful for companies, as the companies could improve their quality of product. Methods: For preprocessing of data TextBlob is used. TextBlob is a package installed in Natural Language Toolkit. The preprocessing steps include tokenization, part of speech tagging and parsing. The stop words are removed using python programming. The stop words corpus which consist of 2400 stop words and which is distributed with NLTK have been used. RapidMiner is used for finding polarity and subjectivity of tweets. TextBlob is used for finding the polarity and subjectivity confidence. Weka is used for calculating the accuracy of tweets based on Naïve Bayes classifier and SVM classifiers. Findings: The paper provides the polarity of tweets which include whether the tweet is positive, negative or neutral. Polarity confidence and subjectivity confidence are also found. Accuracy of tweets are found using Naïve Bayes and SVM classifiers. Applications: Sarcasm Detection could be helpful in analyzing the exact opinion of the user about a certain thing.

Keywords

Polarity, Polarity Confidence, Sarcasm Detection, Subjectivity, Subjectivity Confidence.

Full Text