Software Metrics Research Laboratoryhttp://hdl.handle.net/10523/5882018-04-26T05:51:04Z2018-04-26T05:51:04ZAn effort prediction model for data-centred fourth-generation-language software developmentvan Koten, Chikakohttp://hdl.handle.net/10523/11362018-02-28T01:28:34Z2011-04-07T03:06:44ZAn effort prediction model for data-centred fourth-generation-language software development
2003-11
van Koten, Chikako
Accurate effort prediction is often an important factor for successful software development. However, the diversity of software development tools observed today has resulted in a situation where existing effort prediction models’ applicability appears to be limited. Data-centred fourth-generation-language (4GL) software development provides one such difficulty. This paper aims to construct an accurate effort prediction model for data-centred 4GL development where a specific tool suite is used. Using historical data collected from 17 systems developed in the target environment, several linear regression models are constructed and evaluated in terms of two commonly used prediction accuracy measures, namely the mean magnitude of relative error (MMRE) and pred measures. In addition, R2, the maximum value of MRE, and statistics of the absolute residuals are used for comparing the models. The results show that models consisting of specification-based software size metrics, which were derived from Entity Relationship Diagrams (ERDs) and Function Hierarchy Diagrams (FHDs), achieve good prediction accuracy in the target environment. The models’ good effort prediction ability is particularly beneficial because specification-based metrics usually become available at an early stage of development. This paper also investigates the effect of developers’ productivity on effort prediction and has found that inclusion of productivity improves the models’ prediction accuracy further. However, additional studies will be required in order to establish the best productivity inclusive effort prediction model.
2011-04-07T03:06:44ZEarly experiences in measuring multimedia systems development effortFletcher, TimMacDonell, StephenWong, B L Williamhttp://hdl.handle.net/10523/11302018-02-27T12:46:01Z2011-04-07T03:06:42ZEarly experiences in measuring multimedia systems development effort
1996-10
Fletcher, Tim; MacDonell, Stephen; Wong, B L William
The development of multimedia information systems must be managed and controlled just as it is for other generic system types. This paper proposes an approach for assessing multimedia component and system characteristics with a view to ultimately using these features to estimate the associated development effort. Given the different nature of multimedia systems, existing metrics do not appear to be entirely useful in this domain; however, some general principles can still be applied in analysis. Some basic assertions concerning the influential characteristics of multimedia systems are made and a small preliminary set of data is evaluated.
2011-04-07T03:06:42ZApplications of fuzzy logic to software metric models for development effort estimationGray, AndrewMacDonell, Stephenhttp://hdl.handle.net/10523/11202018-02-27T12:40:28Z2011-04-07T03:06:38ZApplications of fuzzy logic to software metric models for development effort estimation
1997-07
Gray, Andrew; MacDonell, Stephen
Software metrics are measurements of the software development process and product that can be used as variables (both dependent and independent) in models for project management. The most common types of these models are those used for predicting the development effort for a software system based on size, complexity, developer characteristics, and other metrics. Despite the financial benefits from developing accurate and usable models, there are a number of problems that have not been overcome using the traditional techniques of formal and linear regression models. These include the non-linearities and interactions inherent in complex real-world development processes, the lack of stationarity in such processes, over-commitment to precisely specified values, the small quantities of data often available, and the inability to use whatever knowledge is available where exact numerical values are unknown. The use of alternative techniques, especially fuzzy logic, is investigated and some usage recommendations are made.
2011-04-07T03:06:38ZMeasurement of database systems: an empirical studyMacDonell, StephenShepperd, MartinSallis, Philiphttp://hdl.handle.net/10523/10922018-02-27T12:45:20Z2011-04-07T03:06:29ZMeasurement of database systems: an empirical study
1996-08
MacDonell, Stephen; Shepperd, Martin; Sallis, Philip
There is comparatively little work, other than function points, that tackles the problem of building prediction systems for software that is dominated by data considerations, in particular systems developed using 4GLs. We describe an empirical investigation of 70 such systems. Various easily obtainable counts were extracted from data models (e.g. number of entities) and from specifications (e.g. number of screens). Using simple regression analysis, prediction systems of implementation size with accuracy of MMRE=21% were constructed. Our work shows that it is possible to develop simple and effective prediction systems based upon metrics easily derived from functional specifications and data models.
2011-04-07T03:06:29ZFULSOME: Fuzzy logic for software metric practitioners and researchersMacDonell, StephenGray, AndrewCalvert, Jameshttp://hdl.handle.net/10523/10882018-02-27T12:40:23Z2011-04-07T03:06:28ZFULSOME: Fuzzy logic for software metric practitioners and researchers
1999-06
MacDonell, Stephen; Gray, Andrew; Calvert, James
There has been increasing interest in recent times for using fuzzy logic techniques to represent software metric models, especially those predicting development effort. The use of fuzzy logic for this application area offers several advantages when compared to other commonly used techniques. These include the use of a single model with different levels of precision for inputs and outputs used throughout the development life cycle, the possibility of model development with little or no data, and its effectiveness when used as a communication tool. The use of fuzzy logic in any applied field however requires that suitable tools are available for both practitioners and researchers---satisfying both interface and functionality related requirements. After outlining some of the specific needs of the software metrics community, including results from a survey of software developers on this topic, the paper describes the use of a set of tools called FULSOME (Fuzzy Logic for Software Metrics). The development of a simple fuzzy logic system by a software metrician and subsequent tuning are then discussed using a real-world set of software metric data. The automatically generated fuzzy model performs acceptably when compared to regression-based models.
2011-04-07T03:06:28ZBayesian statistical effort prediction models for data-centred 4GL software developmentvan Koten, ChikakoGray, Andrewhttp://hdl.handle.net/10523/10422018-02-27T12:50:27Z2011-04-07T03:06:14ZBayesian statistical effort prediction models for data-centred 4GL software development
2005-11
van Koten, Chikako; Gray, Andrew
Constructing an accurate effort prediction model is a challenge in Software Engineering. This paper presents three Bayesian statistical software effort prediction models for database-oriented software systems, which are developed using a specific 4GL tool suite. The models consist of specification-based software size metrics and development team’s productivity metric. The models are constructed based on the subjective knowledge of human expert and calibrated using empirical data collected from 17 software systems developed in the target environment. The models’ predictive accuracy is evaluated using subsets of the same data, which were not used for the models’ calibration. The results show that the models have achieved very good predictive accuracy in terms of MMRE and pred measures. Hence it is confirmed that the Bayesian statistical models can predict effort successfully in the target environment. In comparison with commonly used multiple linear regression models, the Bayesian statistical models’ predictive accuracy is equivalent in general. However, when the number of software systems used for the models’ calibration becomes smaller than five, the predictive accuracy of the best Bayesian statistical models are significantly better than the multiple linear regression model. This result suggests that the Bayesian statistical models would be a better choice when software organizations/practitioners do not posses sufficient empirical data for the models’ calibration. The authors expect those findings encourage more researchers to investigate the use of Bayesian statistical models for predicting software effort.
2011-04-07T03:06:14ZAlternatives to regression models for estimating software projectsMacDonell, StephenGray, Andrewhttp://hdl.handle.net/10523/10352018-02-27T12:36:58Z2011-04-07T03:06:12ZAlternatives to regression models for estimating software projects
1996-09
MacDonell, Stephen; Gray, Andrew
The use of ‘standard’ regression analysis to derive predictive equations for software development has recently been complemented by increasing numbers of analyses using less common methods, such as neural networks, fuzzy logic models, and regression trees. This paper considers the implications of using these methods and provides some recommendations as to when they may
be appropriate. A comparison of techniques is also made in terms of their modelling capabilities with specific reference to function point analysis.
2011-04-07T03:06:12ZEstablishing relationships between specification size and software process effort in CASE environmentsMacDonell, Stephenhttp://hdl.handle.net/10523/10222018-02-28T01:36:16Z2011-04-07T03:06:08ZEstablishing relationships between specification size and software process effort in CASE environments
1995-07
MacDonell, Stephen
Advances in software process technology have rendered many existing methods of size assessment and effort estimation inapplicable. The use of automation in the software process, however, provides an opportunity for the development of more appropriate software size-based effort estimation models. A specification-based size assessment method has therefore been developed and tested in relation to process effort on a preliminary set of systems. The results of the analysis confirm the assertion that, within the automated environment class, specification size indicators (that may be automatically and objectively derived) are strongly related to process effort requirements.
2011-04-07T03:06:08Z