Assessing the Usability of a Visual Tool for the definition of E-learning Processes

In this paper, we present a usability study aiming at assessing a visual language-based tool for developing adaptive e-learning processes. The tool implements the adaptive self-consistent learning object SET (ASCLO-S) visual language, a special case of flow diagrams, to be used by instructional designers to define classes of learners through stereotypes and to specify the more suited adaptive learning process for each class of learners. The usability study is based on the combined use of two techniques: a questionnaire-based survey and an empirical analysis. The survey has been used to achieve feedbacks from the subjects' point of view. In particular, it has been useful to capture the perceived usability of the subjects. The outcomes show that both the proposed visual notation and the system prototype are suitable for instructional designers with or without experience on the computer usage and on tools for defining e-learning processes. This result is further confirmed by the empirical analysis we carried out by analysing the correlation between the effort to develop adaptive e-learning processes and some measures suitable defined for those processes. Indeed, the empirical analysis revealed that the effort required to model e-learning processes is not influenced by the experience of the instructional designer with the use of e-learning tools, but it only depends on the size of the developed process

Using Functional Complexity Measures in Software Development Effort Estimation

Several definitions of measures that aim at representing the size of software requirements are currently available. These measures have gained a quite relevant role, since they are one of the few types of objective measures upon which effort estimation can be based. However, traditional Functional Size Measures do not take into account the amount and complexity of elaboration required, concentrating instead on the amount of data accessed or moved. This is a problem since the amount and complexity of the required data elaboration affect the implementation effort, but are not adequately represented by the current size measures, including the standardized ones. Recently, a few approaches to measuring aspects of user requirements that are supposed to be related with functional complexity and/or data elaboration have been proposed by researchers. In this paper, we take into consideration some of these proposed measures and compare them with respect to their ability to predict the development effort, especially when used in combination with measures of functional size. A few methods for estimating software development effort \u2013both based on model building and on analogy\u2013 are experimented with, using different types of functional size and elaboration complexity measures. All the most significant models obtained were based on a notion of computation density that is based on the number of computation flows in functional processes. When using estimation by analogy, considering functional complexity in the selection of analogue projects improved accuracy in all the evaluated cases. In conclusion, it appears that functional complexity is a factor that affects development effort; accordingly, whatever method is used for effort estimation, it is advisable to take functional complexity into due consideration

Effort Estimation for Service-Oriented Computing Environments

The concept of service in Service-Oriented Architecture (SOA) makes possible to introduce other ideas like service composition, governance and virtualization. Each of these ideas, when exercised to an enterprise level, provides benefits in terms of cost and performance. These ideas bring many new opportunities for the project managers in making the estimates of effort required to produce SOA systems. This is because the SOA systems are different from traditional software projects and there is a lack of efficient metrics and models for providing a high level of confidence in effort estimation. Thus, in this paper, an efficient estimation methodology has been presented based on analyzing the development phases of past SOA based software systems. The objective of this paper is twofold: first, to study and analyze the development phases of some past SOA based systems; second, to propose estimation metrics based on these analyzed parameters. The proposed methodology is facilitated from the use of four regression(s) based estimation models. The validation of the proposed methodology is cross checked by comparing the predictive accuracy, using some commonly used performance measurement indicators and box-plots evaluation. The evaluation results of the study (using industrial data collected from 10 SOA based software systems) show that the effort estimates obtained using the multiple linear regression model are more accurate and indicate an improvement in performance than the other used regression models

Pragmatic cost estimation for web applications

Cost estimation for web applications is an interesting and difficult challenge for researchers and industrial practitioners. It is a particularly valuable area of ongoing commercial research. Attaining on accurate cost estimation for web applications is an essential element in being able to provide competitive bids and remaining successful in the market. The development of prediction techniques over thirty years ago has contributed to several different strategies. Unfortunately there is no collective evidence to give substantial advice or guidance for industrial practitioners. Therefore to address this problem, this thesis shows the way by investigating the characteristics of the dataset by combining the literature review and industrial survey findings. The results of the systematic literature review, industrial survey and an initial investigation, have led to an understanding that dataset characteristics may influence the cost estimation prediction techniques. From this, an investigation was carried out on dataset characteristics. However, in the attempt to structure the characteristics of dataset it was found not to be practical or easy to get a defined structure of dataset characteristics to use as a basis for prediction model selection. Therefore the thesis develops a pragmatic cost estimation strategy based on collected advice and general sound practice in cost estimation. The strategy is composed of the following five steps: test whether the predictions are better than the means of the dataset; test the predictions using accuracy measures such as MMRE, Pred and MAE knowing their strengths and weaknesses; investigate the prediction models formed to see if they are sensible and reasonable model; perform significance testing on the predictions; and get the effect size to establish preference relations of prediction models. The results from this pragmatic cost estimation strategy give not only advice on several techniques to choose from, but also give reliable results. Practitioners can be more confident about the estimation that is given by following this pragmatic cost estimation strategy. It can be concluded that the practitioners should focus on the best strategy to apply in cost estimation rather than focusing on the best techniques. Therefore, this pragmatic cost estimation strategy could help researchers and practitioners to get reliable results. The improvement and replication of this strategy over time will produce much more useful and trusted results.Cost estimation for web applications is an interesting and difficult challenge for researchers and industrial practitioners. It is a particularly valuable area of ongoing commercial research. Attaining on accurate cost estimation for web applications is an essential element in being able to provide competitive bids and remaining successful in the market. The development of prediction techniques over thirty years ago has contributed to several different strategies. Unfortunately there is no collective evidence to give substantial advice or guidance for industrial practitioners. Therefore to address this problem, this thesis shows the way by investigating the characteristics of the dataset by combining the literature review and industrial survey findings. The results of the systematic literature review, industrial survey and an initial investigation, have led to an understanding that dataset characteristics may influence the cost estimation prediction techniques. From this, an investigation was carried out on dataset characteristics. However, in the attempt to structure the characteristics of dataset it was found not to be practical or easy to get a defined structure of dataset characteristics to use as a basis for prediction model selection. Therefore the thesis develops a pragmatic cost estimation strategy based on collected advice and general sound practice in cost estimation. The strategy is composed of the following five steps: test whether the predictions are better than the means of the dataset; test the predictions using accuracy measures such as MMRE, Pred and MAE knowing their strengths and weaknesses; investigate the prediction models formed to see if they are sensible and reasonable model; perform significance testing on the predictions; and get the effect size to establish preference relations of prediction models. The results from this pragmatic cost estimation strategy give not only advice on several techniques to choose from, but also give reliable results. Practitioners can be more confident about the estimation that is given by following this pragmatic cost estimation strategy. It can be concluded that the practitioners should focus on the best strategy to apply in cost estimation rather than focusing on the best techniques. Therefore, this pragmatic cost estimation strategy could help researchers and practitioners to get reliable results. The improvement and replication of this strategy over time will produce much more useful and trusted results

A novel model for improving the maintainability of web-based systems

Web applications incorporate important business assets and offer a convenient way for businesses to promote their services through the internet. Many of these web applica- tions have evolved from simple HTML pages to complex applications that have a high maintenance cost. This is due to the inherent characteristics of web applications, to the fast internet evolution and to the pressing market which imposes short development cycles and frequent modifications. In order to control the maintenance cost, quantita- tive metrics and models for predicting web applications’ maintainability must be used. Maintainability metrics and models can be useful for predicting maintenance cost, risky components and can help in assessing and choosing between different software artifacts. Since, web applications are different from traditional software systems, models and met- rics for traditional systems can not be applied with confidence to web applications. Web applications have special features such as hypertext structure, dynamic code generation and heterogenousity that can not be captured by traditional and object-oriented metrics. This research explores empirically the relationships between new UML design met- rics based on Conallen’s extension for web applications and maintainability. UML web design metrics are used to gauge whether the maintainability of a system can be im- proved by comparing and correlating the results with different measures of maintain- ability. We studied the relationship between our UML metrics and the following main- tainability measures: Understandability Time (the time spent on understanding the soft- ware artifact in order to complete the questionnaire), Modifiability Time(the time spent on identifying places for modification and making those modifications on the software artifact), LOC (absolute net value of the total number of lines added and deleted for com- ponents in a class diagram), and nRev (total number of revisions for components in a class diagram). Our results gave an indication that there is a possibility for a relationship to exist between our metrics and modifiability time. However, the results did not show statistical significance on the effect of the metrics on understandability time. Our results showed that there is a relationship between our metrics and LOC(Lines of Code). We found that the following metrics NAssoc, NClientScriptsComp, NServerScriptsComp, and CoupEntropy explained the effort measured by LOC(Lines of Code). We found that NC, and CoupEntropy metrics explained the effort measured by nRev(Number of Revi- sions). Our results give a first indication of the usefulness of the UML design metrics, they show that there is a reasonable chance that useful prediction models can be built from early UML design metrics