A data mining‐based technique is proposed for the selection and employment of the best‐fit programmers to meet the needs of software companies. The proposed technique incorporates Bayes' theorem and artificial neural network (ANN). The datasets used were from two software companies (Company 1 and Company 2) in India, covering the years 2010–2015. Bayes' theorem is used for identifying the prognostic attributes of the best‐fit programmers, while the ANN classifier is used for predicting the best‐fit programmers. Using a confusion matrix, the ANN classifier performance is 97.2 and 87.3%, 95.8 and 54.5%, and 100 and 75% with regard to accuracy, precision, and recall on the two test datasets of Company 1 and Company 2, respectively. The results show that the technique is effective for predicting the best‐fit programmers. Software companies can use this technique in their recruitment and selection process to determine the best‐fit employees for the programmer posts. The proposed technique can also be adapted for application in other disciplines such as sports, education, etc., to identify the most suitable person to fill a relevant position.

Software companies have to manage different software projects based on different time, cost, and manpower requirement, which is a very complex task in software project management. Accurate software estimates at the early phase of software development is one of the crucial objectives and a great challenge in software project management, in the last decades. Since software development attributes are vague and uncertain at the early phase of development, software estimates tend to a certain degree of estimation error. A software development cost estimation model incorporates soft computing techniques provides a solution to fit the vagueness and uncertainty of software attributes. In this paper, an adaptive artificial neural network (ANN) architecture for Constructive Cost Model (COCOMO) is proposed in order to produce accurate software estimates. The ANN is utilized to determine the importance of calibration of the software attributes using past project data in order to produce accurate software estimates. Software project data from the COCOMO I and NASA'93 data sets were used in the evaluation of the proposed model. The result shows an improvement in estimation accuracy of 8.36% of the ANN-COCOMO II when compared with the original COCOMO II.

Software development time and cost estimation are the process of estimating the most realistic use of time and cost required for developing a software. It is one of the biggest challenges in the area of software engineering and project management, in the last decades. The software estimates are difficult to obtain due to incomplete software information is available in the early phase of software development process. Insufficient software information causes inaccuracy in software attributes. Thus, the vagueness and uncertainty of the software attributes is the main reason of inaccuracy of software estimates. Software cost estimation models such as regression model, expert judgment, SLIM, and COCOMO require accurate software attributes and long term estimation process, which are not completely achievable in early phase of software development process. Soft computing techniques such as fuzzy logic can reduce the vagueness and uncertainty of software attributes. Therefore, it may consider as alternative to decrease the inaccuracy of software estimates. This research aims to utilise an adaptive fuzzy logic model to improve the accuracy of software time and cost estimation. Using advantages of fuzzy set and fuzzy logic can produce accurate software attributes which result in precise software estimates. The Two-Dimension Gaussian Membership Function (2-D GMF) was used in the fuzzy model to make software attributes smoother in terms of the range of values. The COCOMO I, NASA98 data sets; and four project data from a software company in Malaysia were used in the evaluation of the proposed Fuzzy Logic COCOMO II (FL-COCOMO II). The evaluation of the obtained results, using Mean of Magnitude of Relative Error (MMRE) and PRED(25%) evaluation techniques, showed that the FL-COCOMO II produced the MMRE less than the original COCOMO and the value of PRED(25%) in the Fuzzy-COCOMO II is higher than the original COCOMO. Furthermore, the FL-COCOMO II showed 8.03% improvement in terms of estimation accuracy using MMRE when compared with the original COCOMO. Using advantages of fuzzy logic such as accurate estimation; adaption; understandability, and etc., can improve the accuracy of software estimates.

There is a lack of published studies on software development in Southeast Asia, which is fast becoming an IT outsourcing haven. This paper presents exploratory survey and case study results on software practices of some software firms in five ASEAN countries (Malaysia, Philippines, Singapore, Thailand and Vietnam), and provides directions for further research on software practices in the ASEAN/Southeast Asian region.

Analyses the outcomes of a study on the requirements review process. Fourteen organizations from both the government and commercial sectors were investigated through interviews and mail questionnaires. The aspects considered include the review team, review process, documents and tools used. The review problems and possible solutions are highlighted from the three aspects of software quality, i.e. people, process and technology.