To achieve this task of SDP, machine learning (ML) techniques have been used by several researchers from the past two decades. With the increasing complexity of software, early prediction of defects, and assurance of good software quality of projects become difficult tasks. Effective models can be generated using the object-oriented (OO) metrics. Source code metrics give useful insights to software quality attributes like cohesion, coupling, size, inheritance, etc, and are extensively used in developing software defect models ( Basili, Briand & Melo, 1996 Singh, Kaur & Malhotra, 2010 Radjenović et al., 2013). Efficient defect prediction helps in the timely identification of areas in software that can lead to defects in software owing to better resource utilization ( Malhotra, 2016). Software Defect Prediction (SDP) deals with uncovering the probable future defects. The performances of oversampling methods are superior to undersampling methods. The study provides a guideline for identifying metrics that are influential for SDP. Random oversampling portrays the best predictive capability of developed defect prediction models. Statistical results advocate the use of resampling methods to improve SDP. The performances of developed models are analyzed using AUC, GMean, Balance, and sensitivity. The impact of 10 resampling methods is analyzed on selected features of 12 object-oriented Apache datasets using 15 machine learning techniques. This study aims at (1) identification of useful metrics in the software using correlation feature selection, (2) extensive comparative analysis of 10 resampling methods to generate effective machine learning models for imbalanced data, (3) inclusion of stable performance evaluators-AUC, GMean, and Balance and (4) integration of statistical validation of results. In addition to this large number of software metrics degrades the model performance. Models trained on imbalanced data leads to inaccurate future predictions owing to biased learning and ineffective defect prediction. Statistics of many defect-related open-source data sets depict the class imbalance problem in object-oriented projects. The development of correct and effective software defect prediction (SDP) models is one of the utmost needs of the software industry.
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The Twilight Saga: Breaking Dawn Part 1 : Release Date, Trailer, Cast & Songs About The Twilight Saga: Breaking Dawn Part 1 In order to escape death at the hands of enemies he and his friend Chand Bardai stabbed each other.Ĭhand Bardai compiled the story of the life of Prithviraj Chauhan in his epic poem Prithviraj Raso. When Mahmud ghori praised him he heard his voice and attacked him. and during the archery competition organized by Mahmud ghori, he displayed his skill. His skill of hitting the target just on the basis of sound made by it come handy. Helped by his court poet and friend Chand Bardai, he is believed to kill Mahmud ghori with his “shabdabhedi baan”. He was treated very badly, his eyes were burnt with red-hot irons and he was made blind. As a result Mahmud ghori again attacked India and in the second battle of Tarain Prithviraj Chauhan was defeated and captured. 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Training is a vital part of building your character in Oblivion. Some programs may work better with un-calibrated sensors-especially those that pass values directly to the steering option of the Move block. The are practical advantages and disadvantages to each. Principle: If the robot has only one light sensor, the line following program will instruct the robot to follow only one side of the line (left or right depending on the formulas used in the program and the orientation of the motors in the robot design).For NXT, many of the programs below use either " Calibrated" or " Un-Calibrated" light sensors. Several of the various programs are discussed and illustrated below. There are dozens (probably hundreds) of different programs that can be written to help the robot follow a line. Whether the line is straight, curved, or a combination of both, the robot will use a simple formula to determine its position in relation to the line and how it should move in order to stay on the line. Most line following programs utilize some formula for determining if the robot should move to the left, or right, in order to stay on the line. Line Following Methods (Example Programs) The code below is more of a pseudocode than any actual code you can copy-and-paste into your program. The getLightPercent() and getLightPortion() functions below may need to be created by you. Once the condition has been met in the until function, then stop the line following task to stop following the line. The purpose of using a separate task for line following is so you can break from the line following based on the use of existing until functions such as untilSonar(), untilTouch(), untilDistance(), wait(), etc. This exercise walks you through the different steps in setting up a line following task and running it from within the main task. If you have RobotC, here is a good exercise in the task of LineFollowing. Line Following RobotC Include File Exercise in Line Following (for RobotC) |
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