Articles

Soft Computing and Machine Intelligence Journal 

Volume 1, Issue 1, (2021)

SCMIJ
Towards Effective Analysis and Tracking of Mozilla and Eclipse Defects using Machine Learning Models based on Bugs Data

Zohaib Hassan (a)*, Naeem Iqbal (a) and Abnash Zaman (b)

(a) FSRA&IT Solutions Providing Organization Peshawar, Pakistan
(b) Faculty of Bioinformatics, Shaheed Benazeer Bhutto Women University Peshawar, Pakistan
* Corresponding author ✉: Dev.zohaibmrt@hotmail.com

Abstract: Analysis and Tracking of bug reports is a challenging field in software repositories mining. It is one of the fundamental ways to explores a large amount of data acquired from defect tracking systems to discover patterns and valuable knowledge about the process of bug triaging. Furthermore, bug data is publically accessible and available of the following systems, such as Bugzilla and JIRA. Moreover, with robust machine learning (ML) techniques, it is quite possible to process and analyze a massive amount of data for extracting underlying patterns, knowledge, and insights. Therefore, it is an interesting area to propose innovative and robust solutions to analyze and track bug reports originating from different open source projects, including Mozilla and Eclipse. This research study presents an ML-based classification model to analyze and track bug defects for enhancing software engineering management (SEM) processes. In this work, Artificial Neural Network (ANN) and Naive Bayesian (NB) classifiers are implemented using open-source bug datasets, such as Mozilla and Eclipse. Furthermore, different evaluation measures are employed to analyze and evaluate the experimental results. Moreover, a comparative analysis is given to compare the experimental results of ANN with NB. The experimental results indicate that the ANN achieved high accuracy compared to the NB. The proposed research study will enhance SEM processes and contribute to the body of knowledge of the data mining field.

DOI:https://doi.org/10.22995/scmi.2021.1.1.01
SCMIJ
Using machine learning algorithms for housing price prediction: The case of Islamabad housing data

Imran (a)* and Umar Zaman (b)

(a) Bahria University Islamabad, Pakistan
(b) Iqra University Islamabad, Pakistan
* Corresponding author ✉: imranjejunu@gmail.com

Abstract: House price prediction is an important financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain.This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages namely Data Collection, Pre Processing the data collected and transforming it to best format, developing intelligent models using machine learning algorithms, training,testing and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores which provide a good estimate of the city housing market. The prediction model can greatly assist in prediction of future housing prices of Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset

DOI:https://doi.org/10.22995/scmi.2021.1.1.03
SCMIJ
Sentiment Analysis to Classify Scams in Crowdfunding

Wafa shafqat(a) and Yung-cheol Byun(a)*

(a) Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
* Corresponding author ✉: ycb@jejunu.ac.kr

Abstract: The accelerated growth of the internet and the enormous amount of data availability has become the primary reason for machine learning applications for data analysis and, more specifically, pattern recognition and decision making. In this paper, we focused on the crowdfunding site Kickstarter and collected the comments in order to apply neural networks to classify the projects based on the sentiments of backers. The power of customer reviews and sentiment analysis has motivated us to apply this technique in crowdfunding to find timely indications and identify suspicious activities and mitigate the risk of money loss.

DOI:https://doi.org/10.22995/scmi.2021.1.1.04
SCMIJ
Improving the Product Recommendation System based-on Customer Interest for Online Shopping Using Deep Reinforcement Learning

Zeinab Shahbazi (a) and Yung-cheol Byun(a)*

(a) Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
* Corresponding author ✉: ycb@jejunu.ac.kr

Abstract: In recent years, due to COVID-19, the process of shopping has become more restricted and difficult for customers. Based on this aspect, customers are more interested in online shopping to keep the Untact rules and stay safe, similarly ordering their product based on their need and interest with most straightforward and fastest ways. In this paper, the reinforcement learning technique is applied in the product recommendation system to improve the recommendation system quality for better and more related suggestions based on click patterns and users' profile information. The dataset used in this system was taken from an online shopping mall in Jeju island, South Korea. We have compared the proposed method with the recent state of the art and research results, which shows that reinforcement learning effectiveness is higher than other approaches.

DOI:https://doi.org/10.22995/scmi.2021.1.1.02
SCMIJ
Effect of Human Related Factors on Requirements Change Management in Offshore Software Development Outsourcing: A theoretical framework

Faisal Mehmood(a) and Sukana Zulfqar(a)*

(a) School of information and communication engineering, Zhengzhou University, China
(b)Department of Computer science, Govt. Post Graduate College of Science, Samanabad Faisalabad, Pakistan.
* Corresponding author ✉:faisalmehmood685@uaf.edu.pk

Abstract: Software development organizations are globalizing their developmentactivities increasingly due to strategic and economic gains. Global software development(GSD) is an intricate concept, and various challenges are associated with it, specificallyrelated to the software requirement change management Process (RCM). This researchaims to identify humans’ related success factors (HSFs) and human-related challenges(HCHs) that could influence the RCM process in GSD organizations and propose atheoretical framework of the identified factors concerning RCM process implementation.The Systematic Literature Review (SLR) method was adopted to investigate the HSFsand HCHs. Using the SLR approach, a total of 10 SFs and 10 CHs were identified. Thestudy also reported the critical success factors (HCSFs) and critical challenges (HCCHs)for RCM process implementation following the factors having a frequency 50% as critical.Our results reveal that five out of ten HSFs and 4 out of ten HCHs are critical for RCMprocess implementation in GSD. Finally, we have developed a theoretical frameworkbased on the identified factors that indicated a relationship among the identified factorsand the implementation of the RCM process in the context of GSD. We believe that theresults of this research can help tackle the complications associated with the RCM in GSDenvironment, which is vigorous to the success and progression of GSD organizations.

DOI:https://doi.org/10.22995/scmi.2021.1.1.05
SCMI Journal

Soft Computing and
Machine Intelligence Journal 

Contact us
  • ✉: info@scmij.com
  • ✆: +82 (0) 10 7490 1947 
Join SCMI