Siew Hock Ow

Proposing a new software cost estimation model based on artificial neural networks

The precision of software project estimation such as project cost estimation, project quality estimation and project risk analysis are important issues in software project management. The ability to accurately estimate software development costs is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle. The estimates are often the least accurate, because very little detail is known about the project and the product at the beginning. The need for reliable and accurate cost predictions in software engineering is an ongoing challenge for software engineers. In this paper a novel neural network Constructive Cost Model (COCOMO) is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

Improving estimation accuracy of the COCOMO II using an adaptive fuzzy logic model

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.

A novel soft computing model to increase the accuracy of software development cost estimation

Software cost and time estimation is the process of estimating the cost and time required to develop a software system. Software cost and time estimation supports the planning and tracking of software projects. Effectively controlling the expensive investment of software development is one of the important issues in software project management. Estimating software development cost with high precision is still a great challenge for project managers, because it allows for considerable financial and strategic planning. Software cost estimation refers to the predictions of the likely amount of effort, time, and staffing levels required to build a software system. A very helpful form of cost estimation is the one made at an early stage during a project, when the costing of the project is proposed for approval. However, estimates at the early stages of the development are the most difficult to obtain. In this paper a novel Constructive Cost Model (COCOMO) based on soft computing approach is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model

Nowadays, mature software companies are more interested to have a precise estimation of software metrics such as project time, cost, quality, and risk at the early stages of software development process. The ability to precisely estimate project time and costs by project managers is one of the essential tasks in software development activities, and it named software effort estimation. The estimated effort at the early stage of project development process is uncertain, vague, and often the least accurate. It is because that very little information is available at the beginning stage of project. Therefore, a reliable and precise effort estimation model is an ongoing challenge for project managers and software engineers. This research work proposes a novel soft computing model incorporating Constructive Cost Model (COCOMO) to improve the precision of software time and cost estimation. The proposed artificial neural network model has good generalisation, adaption capability, and it can be interpreted and validated by software engineers. The experimental results show that applying the desirable features of artificial neural networks on the algorithmic estimation model improves the accuracy of time and cost estimation and estimated effort can be very close to the actual effort.

Project management practices: Success versus failure

Often, software managers have to monitor and manage many projects concurrently. Unfortunately, some projects were completed successfully but some were not completed on time, over budget or being cancelled. Some of the reasons of this project failure are: lack of user involvement, lack of planning, incomplete requirements, lack of resources, incorrect cost estimation, etc. There are many project planning and scheduling techniques to manage and help to ensure project success. Some of these techniques, however, may not be suitable for specific types of projects and thus, cause projects to fail. This paper discusses the issues involved in project success and failure, and presents the results of seven projects undertaken by the undergraduate students taking the course project management.

Wireless intelligent incontinence management system using smart diapers

This paper presents a wireless intelligent incontinence management system being developed for the University Malaya Medical Center (UMMC) that utilizes ldquosmartrdquo diapers to discreetly monitor and estimate wetness events, detect other relevant clinical conditions and alert staff by transmitting information via wireless technology to an intelligent central management system. We describe the hardware and software modules of the system and give results conducted on diaper experiments and the transmission range of the wireless technology.

Eliciting essential requirements for social networks in academic environments

Recent years have witnessed the generation of online social networking web sites, in which millions of members publicly articulate mutual friendship relations and share user-created contents. But it is just a few years that we have seen some efforts to create special social networks to be used in academic environments. Considering the increasing needs for this kind of web sites, we did a comprehensive survey on more than 20 academic social networks for gathering and categorizing the essential requirements for this kind of web sites. Hence, having some good experiences by creating the Iranian Experts Social Network with more than 120,000 official members, in this paper we try to clarify the features of Social Networks in academic environments and propose a category for their requirements.

Proposing a New High Performance Model for Software Cost Estimation

Software development effort estimation is the process of predicting the most realistic use of effort required for developing software based on some parameters. It has always characterised one of the biggest challenges in Computer Science for the last decades. Because time and cost estimate at the early stages of the software development are the most difficult to obtain, and they are often the least accurate. Traditional algorithmic techniques such as regression models, Software Life Cycle Management (SLIM), COCOMO model, function points, etc, require an estimation process in a long term. But, nowadays that is not acceptable for software developers and companies. Newer soft computing techniques to effort estimation based on non-algorithmic techniques such as Fuzzy Logic (FL) may offer an alternative for solving the problem. This work aims to propose a fuzzy logic realistic model to achieve more accuracy in software effort estimation. In this innovative model, by applying fuzzy logic and using training procedure to the system, the accuracy of the results is desirable in comparison with the famous traditional algorithmic technique, COCOMO II model. This novelty model will lead researchers to focus on non-algorithmic models to overcome the estimation problems. Our validation experiment was carried out on artificial dataset as well as the COCOMO standard dataset.

A study on the requirements review process in software development: problems and solutions

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.

A survey on software quality assurance-a Malaysian perspective

The paper presents the findings of a survey on aspects of software quality assurance (SQA). Fifty-two sets of questionnaires were collected for analysis. Brief discussions on the survey methodology, data processing and analysis are included. Highlights of this survey encompass the trends of SQA investments, QA problems encountered, the tools being used and their weaknesses, as well as the people who are involved in SQA activities.