ANTECEDENTS OF ORGANIZATIONAL COMMITMENT OF BANKING SECTOR EMPLOYEES IN PAKISTAN

The aim of this study was to check the association of factors like work environment, job security,pay satisfaction and participation in decision making; with organizational commitment of theemployees, working in the banking sector of Pakistan. Two hundred and fifteen (215) responses toquestionnaire-based survey were collected from managerial and non-managerial employees, andanalyzed. The analysis showed positive correlations between the dependent and independentvariables. The relation between job security and organizational commitment was the most significant,indicating that a secure job can yield higher level of commitment. Work environment also had asignificant relation with organizational commitment, showing that a healthy and friendly workenvironment may enhance an employee’s commitment towards his work and organization. Paysatisfaction and participation in decision-making had low correlations with organizationalcommitment. Age and tenure seemed to affect the commitment of employees, with highercommitment shown for higher age and tenure; whereas gender did not show significant change incommitment level of employees

Machine Learning Model to Predict Automated Testing Adoption

Software testing is an activity conducted to test the software under test. It has two approaches: manual testing and automation testing. Automation testing is an approach of software testing in which programming scripts are written to automate the process of testing. There are some software development projects under development phase for which automated testing is suitable to use and other requires manual testing. It depends on factors like project requirements nature, team which is working on the project, technology on which software is developing and intended audience that may influence the suitability of automated testing for certain software development project. In this paper we have developed machine learning model for prediction of automated testing adoption. We have used chi-square test for finding factors’ correlation and PART classifier for model development. Accuracy of our proposed model is 93.1624%.</p