A Rough-Fuzzy Inference System for Selecting Team Leader for Software Development Teams

2017 ◽  
pp. 304-314 ◽  
Author(s):  
Jafreezal Jaafar ◽  
Abdul Rehman Gilal ◽  
Mazni Omar ◽  
Shuib Basri ◽  
Izzatdin Abdul Aziz ◽  
...  
Keyword(s):  
Software Development ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Team Leader ◽  
Inference System ◽  
Development Teams ◽  
Software Development Teams

scholarly journals Investigating the Use of an Adaptive Neuro-Fuzzy Inference System in Software Development Effort Estimation

2021 ◽  
pp. 18-24
Author(s):  
Zainab Rustum Mohsin
Keyword(s):  
Software Development ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Computational Technique ◽  
Development Effort ◽  
Effort Estimation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Modeling software development effort estimation models has been a hot research topic over the last three decades. Numerous models were proposed in these decades to predict the effort. The key challenges for future software development is providing accurate software estimation. Failure to acknowledge the accuracy of effort estimation can cause inaccurate estimation, customer disappointment, and poor software development or project failure. This research presents a novel computational technique, named adaptive neuro-fuzzy inference system (ANFIS), for the modeling of software effort estimation. It was developed utilizing the Constructive Cost Model (COCOMO) dataset. The data were randomly divided into two sets: 83% for training and 17% for testing. The mean magnitude relative-error (MMRE) and the coefficient of correlation (R) were used as assessment indices. Results showed that the accuracy of the proposed model is quite satisfactory in comparison with actual values. Moreover, a comparison study was conducted with another approach. The results showed that ANFIS produced better results in comparison with Function Point Analysis, Software Lifecycle Management, and COCOMO methods. ANFIS was found to be a potential predictive model for software development effort estimation.

An optimized Software defect prediction model based on PSO - ANFIS

2020 ◽  
Vol 13 ◽  
Author(s):  
Misha Kakkar ◽  
Sarika Jain ◽  
Abhay Bansal ◽  
P.S. Grover
Keyword(s):  
Software Development ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Anfis Model ◽  
Inference System ◽  
Model Based ◽  
Software Defect ◽  
Novel Approach ◽  
Development Teams ◽  
Software Development Teams

Introduction : The Software defect prediction (SDP) model plays a very important role in today’s software industry. SDP models can provide either only a list of defect-prone classes as output or the number of defects present in each class. This output can then be used by quality assurance teams to effectively allocate limited resources for validating software products by putting more effort into these defect-prone classes.The study proposes an OANFIS-SDP model that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. Method: OANFIS is a novel approach based on the Adaptive neuro-fuzzy inference system (ANFIS), which is optimized using Particle swarm optimization (PSO). OANFIS model combines the flexibility of ANFIS model with the optimization capabilities of PSO for better performance. Results: The proposed model is tested using the dataset from open source java projects of varied sizes (from 176 to 745 classes). Conclusion: The study proposes an SDP model based OANFIS that gives the number of defects as an output to software development teams. Development teams can then use this data for better allocation for their scares resources such as time and manpower. OANFIS is a novel approach that uses the flexibility provided by the ANFIS model and optimizes the same using PSO. The results given by OANFIS are very good and it can also be concluded that the performance of the SDP model based on OANFIS might be influenced by the size of projects. Discussion: The performance of the SDP model based on OANFIS is better than the ANFIS model. It can also be concluded that the performance of the SDP model might be influenced by the size of projects.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

ZERO-DAY POLYMORPHIC CYBERATTACKS DETECTION USING FUZZY INFERENCE SYSTEM

2020 ◽  
pp. 8-13
Author(s):  
V. V. Fesokha ◽  
I. Y. Subach ◽  
V. O. Kubrak ◽  
A. V. Mykytiuk ◽  
S. O. Korotaiev
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System
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