Study on system non-functional type response : user interface and batch processing

• Main Author: Vellingiri Naveen Prasath
• Other Authors: Zhou Xing
• Format: Theses and Dissertations
• Language: English
• Published: 2018
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/76089
• Institution: Nanyang Technological University

Software Development Life Cycle (SDLC) involves a set of tasks performed by software industry to design, develop and test the developed software. Testing methods such as functional and non-functional testing are an inevitable part of software development life cycle. It is common to have bugs and the count of bugs can be reduced by testing them from the initial stage of software development. Functional testing is done by developers during software development Unit testing and integration testing require coding skills, but other functional testing methods like black box testing and user acceptance testing do not require coding skills. The In-House System (IHS) developed in my department, practices functional testing methods namely unit testing, integration testing, white box testing, black box testing, regression testing, and user acceptance testing. Nonfunctional Testing (NFT) of the software measures the behavior in terms of agility when prone to a given input and helps in determining the performance of the software. Software testing should have both functional testing and non-functional testing methods indulged in order to maintain good standards. For example, software should be tested in terms of its functionality, reliability, and scalability. Each transaction performed inside IHS is recorded with a response time, uniform resource locator URL's, time stamp, user history, data consumed and Code ID as log files. The main aim of this dissertation is to get the transactions in the In-house systems which have higher response time using the log files data. The transactions which have higher response time are found by means of python coding on a daily and weekly basis. Graphical analysis is done on the transactions which have higher response time by considering the URL, data consumed and user history. A study on the reason for obtaining the higher response time is performed as well.