Extraction cost of quality and testing in software project

Implementation of quality and testing by outsourced test team is new field procurement in software project especially in software development. In Government Agency of Malaysia, cost procurement for implementing both of the quality and testing are blended together with the cost of overall project which is implemented by software development team. Therefore, the cost of quality and testing has to extract from the overall cost of software project. The problem is how to estimate the cost of quality and testing that will be provided to outsourced test team. This paper aim to extract the cost of quality and testing from the total cost of the software project based on Salleh and Primandaria model. The result shows that our extraction model produces an acceptable estimation for project and suitable apply by Government Agency of Malaysia

SMMS: document management in agile model for software maintenance

Study shows that most challenging task in software development life cycle is software maintenance,because all of changes made must documented for future used. Thus, an efficient and newest documentation is vital in software maintenance to ensure its activities will not jeopardized. However, this is not as we thought. The popular models such as agile development has ruined the importance of maintenance tasks in software development life cycle. The agile models focused only on verbal communication within development team to provide faster development where the information of the product and its features exists within the heads of the developers but any changes will not documented in a design document. Therefore, the major problem of Agile models is the absence of latest documentation and this issue supposed not exist in software maintenance which is depend on previous documentation. Thus, our aim is to add document management in agile model for software maintenance processes. The result shows that our concept is successful and useful for software maintenance

Verifying the Correctness of UML Statechart Outpatient Clinic Based on Common Modeling Language and SMV

Unified-modelling language (UML) is a standard general purpose modelling language, which is widely, used in system design of banking, biological, plantation and healthcare. Recently, there are many systems of healthcare are modeled using behavioral diagram such as UML statechart for design purposes. However, the behavior of healthcare statechart is rarely verified to ensure it is behaving as we needed. In software engineering, a software should be verified before it is transform to the further phases. In this paper, a statechart of outpatient clinic is verified to ensuring the correctness of its design. Therefore, to achieve our objective, we have applied Common Modeling Language (CML) and SMV model checker for verification formal system modeling and specification of property of statechart outpatient clinic. The result shows that the statechart of outpatient clinic is behave as required and the statechart is allowable to transform to the next phase

Cross-project software defect prediction

The feasibility of building a software defect prediction (SDP) model in the absence of previous records has been increased by the introduction of the Cross-Project Defect Prediction (CPDP) method. Although this method overcomes the limitations of SDP in the absence of previous historical records, the predictive performance of the CPDP model is relatively poor due to distribution discrepancy between the source and the target datasets. To overcome this challenge, various studies have been published. This SLR was conducted after analyzing research articles published since 2013 in four digital libraries: Scopus, IEEE, Science Direct, and Google Scholar. In this work, five research questions covering the classification algorithms, dataset, independent variables, performance evaluation metrics used in CPDP studies, and as well as the performance of individual machine learning classification algorithms in predicting software defects across different software projects were addressed accordingly. To respond to outlined questions, 34 most relevant articles were selected after passing through quality assessment criteria. Through this work, it was discovered the majority of the selected studies used machine learning techniques as classification algorithms, and 64% of the studies used the combination of Object-Oriented (OO) and Line of Code (LOC) metrics. All the selected studies used publicly available datasets from NASA, PROMISE, SOFLAB, AEEEM, and Relink. The most commonly used evaluation metrics are F_measure and AUC. Best performing classifiers include Logistic Regression and SVM. Despite various efforts to improve the performance of the CPDP model, the performance is below the applicable level. Thus, there is a need for further study that will improve the performance of the CPDP model