A Novel Way of Assessing Software Bug Severity Using Dictionary of Critical Terms

Sharma, Gitika; Sharma, Sumit; Gujral, Shruti

doi:10.1016/j.procs.2015.10.059

Cited by 50 publications

(23 citation statements)

References 15 publications

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“…Dual networks examined wavelets are by and large orthonormal. Utilizing the premise work in [26], DWT can be communicated as the internal item between the sunlight based PV flag x(t) and the premise work as. Tm,n is the coefficient of wavelet at level (or enlargement) m and area (or interpretation) n, and gives the detail (fine data) introduced in the flag.…”

Section: Wavelet Decompositionmentioning

confidence: 99%

A Review of Fuzzy Logic and Artificial Neural Network Technologies Used for MPPT

Wani¹

2021

TURCOMAT

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Solar electric power generating stations play a major role in meeting the growing demand for electric power. These generating stations make use of solar photovoltaic (PV) panels to perform the conversion of solar energy to electric energy. However, the solar panel output is highly unpredictable because the output is a function of number of factors; some of which are not in the control of humans like the weather conditions, and the output is also a function of the age of PV panel, dust and other debris collected on the panel, direction and angle of elevation and so on. The solar panels exhibit a low efficiency. Currently, a lot of research is going on to overcome these issues. This paper represents a review of two modern techniques used in solar photovoltaic systems which enhance the extraction of maximum output power in an efficient manner. The Artificial Intelligence Based MPPT Techniques for PV Applications, and, a Forecasting System of Solar PV Power Generation using Wavelet Decomposition and Bias- compensated Random Forest are reviewed and compared in this paper.

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Section: Wavelet Decompositionmentioning

confidence: 99%

A Review of Fuzzy Logic and Artificial Neural Network Technologies Used for MPPT

Wani¹

2021

TURCOMAT

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“…Simpson SJ et.al [46] gave a survey on this procedure that despite the fact that grasshopper are generally observed exclusively in nature ,they participate in one of the biggest swarm of all creatures.Rogers SM et. al [47]investigated the novel part of grasshopper swarm for example the swarming nature lie in both sprite and adulthood. Anas Atef Amaireh et.…”

Section: Ai= μEwmentioning

confidence: 99%

Analysis of Various Optimization Techniques in Machine Learning

Kaur¹

2021

TURCOMAT

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Optimization model the goal of solving a task in the paramount way providing the best results and it implies a best view point for solving query or we can also define optimization as a way of probing an another solution with the maximum cost efficient and utmost practicable performance under the required constraints, by increasing the preferred factor and reducing the unprefferable ones. This increase provides the utmost result or inference without any obstacles or complications. In this paper, various optimization techniques with respect to image processing are studied, where image processing deals with certain tasks and they are Image segmentation, image compression, image enhancement, image restoration (in painting).This paper provides basic knowledge of some optimization techniques.

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“… Support Vector Machine SVM is a statistical and mathematical technique and gives the effective result on the field of recognition and classification of objects. This techniques also gives effective result in the field of text mining and avoid the problem of dimensionality because it works on high dimensions of data [12,13]. The report sets are separated in the given categories which depends upon the hyperplane having maximum margin.…”

Section:  Naïve Bayes Multinomialmentioning

confidence: 99%

“…Consequently, in specific cases, the whole settling technique is slowed down because of the presence of a blocking bug task, copy bug report discovery, bug settling time forecast, and revived bug expectation. Gitika Sharma et al [13]: Software is affecting gigantic human exercises and its utilization is ascending at an exceptional rate. On account of increment popular and diminished in conveyance time giving the surety of value while diminishing conveyance time is ends up basic.…”

Section: Related Workmentioning

confidence: 99%

Improved Framework for Bug Severity Classification using N-gram Features with Convolution Neural Network

Kaur*¹,

Dutta²

2019

IJRTE

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Foreseeing the seriousness/severity of bugs has been established in former research study in order to recover triaging and the process of bug resolution. Therefore, numerous prediction/classification methodologies were developed throughout the years to give an automated reasoning over the seriousness classes. Seriousness or severity is a significant trait of a bug that chooses how rapidly it ought to be measured. It causes designers to comprehend significant bugs on schedule. Though, manual evaluation of severity is a dreary activity and could be off base. This paper comprises of using the text/content mining together along with the use feature selection and bi-grams to improve the order of bugs in six classes. In the proposed methodology the features are refined by the use of convolution layers. Here, the process of convolution-based refining indicates mapping of the features utilizing non-linear methods of all the classes as compared to the existing methodologies.

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A Novel Way of Assessing Software Bug Severity Using Dictionary of Critical Terms

Cited by 50 publications

References 15 publications

A Review of Fuzzy Logic and Artificial Neural Network Technologies Used for MPPT

A Review of Fuzzy Logic and Artificial Neural Network Technologies Used for MPPT

Analysis of Various Optimization Techniques in Machine Learning

Improved Framework for Bug Severity Classification using N-gram Features with Convolution Neural Network

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