International Journal of Computational Systems Engineering
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International Journal of Computational Systems Engineering (3 papers in press)
Towards Recent Developments in the Methods, Metrics and Datasets of Software Fault Prediction by Deepak Sharma, Pravin Chandra Abstract: The world of software systems is amplified with the changing environment magnifying the demand for quality software. Software fault prediction is a requisite activity ensuring the development of economic, efficient and quality software. It is the procedure for the development of models which help to identify faults in modules during early phases of software development lifecycle. Software fault prediction is one of the most prevalent research disciplines. The existing study in this domain includes numerous modeling techniques and software metrics for the early predictions of software faults. This paper aims to explore some of the prominent studies for software fault prediction in the existing literature. In this paper, software fault prediction papers since 1990 to 2017 are investigated. The paper includes the analysis of the studies having empirical validation and a good source of publication. The paper reflects the methods, metrics, and datasets available in the literature for software fault prediction. In addition, the modeling techniques based on traditional and computational intelligence based methods are also reviewed. This paper is an endeavor to assemble the existing techniques and metrics of software fault prediction with a motive to assist researchers for easy evaluation of suitable metrics for their own research scenarios. Keywords: Software Fault Prediction; Fault Tolerance; Computational Intelligence; Software Metrics; Evaluation Metrics.
Comparing Robustness of Realised Measures under Round-off Errors, Price Adjustments and Serial Correlations: A Simulation Study by Hiroumi Misaki Abstract: We compare the accuracy of realised measures using a number of computer simulations. Realised measures are the methods used to estimate the integrated volatility from high-frequency data. We consider a simple realised volatility (RV), a 5-minnute RV, a subsampled 5-minute RV, a two-scale estimator (TS), a realised kernel (RK), a pre-averaging estimator (PA) and a separating information maximum likelihood estimator (SIML). We used seven market microstructure models, which included round-off errors, price adjustments and serial correlation. The SIML is not irrationally biased in any case; this implies that the SIML is sufficiently robust to the market microstructure noise in any form. We have also found that the SIML is the only realised measure for maintaining consistency in all our simulations. We conclude that SIML is suitable for practical applications. Keywords: finance; high frequency data; decision making; realised measures; volatility estimation; robustness; market microstructure noise; round-off; price adjustments; serial correlations; simulation study; high performance computing; separating information maximum likelihood; SIML.
A LITERATURE SURVEY ON THE PERFORMANCE PREDICTION OF MUTUAL FUNDS by Shikha Singla, Gaurav Gupta Abstract: Mutual funds are excellent medium for investors to invest, who dont have much know-how about financial markets. Investors generally take Funds historical performance and their funds rating as harbinger for its performance prediction. This myopic selection and prediction criterion sometimes leads to wrong funds allocation and hence poor portfolio performance. Performance prediction depends upon number of controlled and uncontrolled factors and very difficult to predict it precisely. However In past, there had been many studies which tried to predict the performance by using various statistical techniques. This review paper covers various different techniques to check the funds performance. Through this survey it is found that a lot of work can be done in the field of performance analysis of mutual fund by taking different factors into consideration. In end, this paper gives a brief literature review of mutual funds performance prediction models. Keywords: Mutual fund; performance evaluation; Net Asset Value (NAV); Data Envelopment Analysis (DEA); Back Propagation Network (BPN).