AN ASSESSMENT OF SOFTWARE DEVELOPMENT PRACTICES OF SMES IN BANGLADESH
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTER SCIENCE AND ELECTRONICS ENGINEERING
Author(s): HASAN SARWAR , MD. AKHTARUZZAMAN , MOHAMMAD SHAHADAT HOSSAIN CHOWDHURY
Abstract: Software Process Improvement (SPI) can help the software practitioners to enhance their capabilities in the field of software engineering. Various models have been proposed in order to improve software process framework. However, implementation of such modules often requires huge cost involvement. Small and Medium sized Software Enterprises (SMEs), in Bangladesh, naturally fail to introduce or adapt such frameworks. So it has become a need of time to observe the Software Process scenario as a part of research activity which will ensure smoother transition to improved Process Framework. In this paper, various practical models and their practices in various SMEs and the importance of SPI system are highlighted and analyzed. Survey has been conducted to assess Real life practices with a view to compare with Benchmarks like Capability Maturity Model Integration (CMMI). This attempt also suggests a direction for minimum best practice that might be helpful for small and medium sized companie
- Publication Date: 09-Mar-2014
- DOI: 10.15224/978-1-63248-000-2-35
- Views: 0
- Downloads: 0
REVIEW OF CURRENT ONLINE DYNAMIC UNSUPERVISED FEED FORWARD NEURAL NETWORK CLASSIFICATION
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTER SCIENCE AND ELECTRONICS ENGINEERING
Author(s): SAMEEM ABDUL KAREEM , HAITHAM SABAH HASAN , ROYA ASADI
Abstract: Online Dynamic Unsupervised Feed Forward Neural Network (ODUFFNN) classification is suitable to be applied in different research areas and environments such as email logs, networks, credit card transactions, astronomy and satellite communications. Currently, there are a few strong methods as ODUFFNN classification, although they have general problems. The goal of this research is an investigation of the critical problems and comparison of current ODUFFNN classification. For experimental results, Evolving Self-Organizing Map (ESOM) and Dynamic Self-Organizing Map (DSOM) as strong related methods are considered; and also they applied some difficult datasets for clustering from the UCI Dataset Repository. The results of the ESOM and the DSOM methods are compared with the results of some related clustering methods. The clustering time is measured by the number of epochs and CPU time usage. The clustering accuracies of methods are measured by employing F-measure through an average of three
- Publication Date: 09-Mar-2014
- DOI: 10.15224/978-1-63248-000-2-36
- Views: 0
- Downloads: 0