Requirements Prioritization Based on Benefit and Cost Prediction: An Agenda for Future Research

In early phases of the software cycle, requirements prioritization necessarily relies on the specified requirements and on predictions of benefit and cost of individual requirements. This paper presents results of a systematic review of literature, which investigates how existing methods approach the problem of requirements prioritization based on benefit and cost. From this review, it derives a set of under-researched issues which warrant future efforts and sketches an agenda for future research in this area

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

Investigating effort prediction of web-based applications using CBR on the ISBSG dataset

As web-based applications become more popular and more sophisticated, so does the requirement for early accurate estimates of the effort required to build such systems. Case-based reasoning (CBR) has been shown to be a reasonably effective estimation strategy, although it has not been widely explored in the context of web applications. This paper reports on a study carried out on a subset of the ISBSG dataset to examine the optimal number of analogies that should be used in making a prediction. The results show that it is not possible to select such a value with confidence, and that, in common with other findings in different domains, the effectiveness of CBR is hampered by other factors including the characteristics of the underlying dataset (such as the spread of data and presence of outliers) and the calculation employed to evaluate the distance function (in particular, the treatment of numeric and categorical data)

Experimental Study Using Functional Size Measurement in Building Estimation Models for Software Project Size

This paper reports on an experiment that investigates the predictability of software project size from software product size. The predictability research problem is analyzed at the stage of early requirements by accounting the size of functional requirements as well as the size of non-functional requirements. The experiment was carried out with 55 graduate students in Computer Science from Concordia University in Canada. In the experiment, a functional size measure and a project size measure were used in building estimation models for sets of web application development projects. The results show that project size is predictable from product size. Further replications of the experiment are, however, planed to obtain more results to confirm or disconfirm our claim

Functional Size Measurement Tool-based Approach for Mobile Game

Nowadays, software effort estimation plays an important role in software project management due to its extensive use in industry to monitor progress, and performance, determine overall productivity and assist in project planning. After the success of methods such as IFPUG Function Point Analysis, MarkII Function Point Analysis, and COSMIC Full Function Points, several other extension methods have been introduced to be adopted in software projects. Despite the efficiency in measuring the software cost, software effort estimation, unfortunately, is facing several issues; it requires some knowledge, effort, and a significant amount of time to conduct the measurement, thus slightly ruining the advantages of this approach. This paper demonstrates a functional size measurement tool, named UML Point tool, that utilizes the concept of IFPUG Function Point Analysis directly to Unified Modeling Language (UML) model. The tool allows the UML eXchange Format (UXF) file to decode the UML model of mobile game requirement and extract the diagrams into component complexity, object interface complexity, and sequence diagram complexity, according to the defined measurement rules. UML Point tool then automatically compute the functional size, effort, time, human resources, and total development cost of mobile game. Besides, this paper also provides a simple case study to validate the tool. The initial results proved that the tool could be useful to improve estimation accuracy for mobile game application development and found to be reliable to be applied in the mobile game industry

Towards making functional size measurement easily usable in practice

Functional Size Measurement methods \u2013like the IFPUG Function Point Analysis and COSMIC methods\u2013 are widely used to quantify the size of applications. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified measurement methods have been proposed. This research explores easily usable functional size measurement method, aiming to improve efficiency, reduce difficulty and cost, and make functional size measurement widely adopted in practice. The first stage of the research involved the study of functional size measurement methods (in particular Function Point Analysis and COSMIC), simplified methods, and measurement based on measurement-oriented models. Then, we modeled a set of applications in a measurement-oriented way, and obtained UML models suitable for functional size measurement. From these UML models we derived both functional size measures and object-oriented measures. Using these measures it was possible to: 1) Evaluate existing simplified functional size measurement methods and derive our own simplified model. 2) Explore whether simplified method can be used in various stages of modeling and evaluate their accuracy. 3) Analyze the relationship between functional size measures and object oriented measures. In addition, the conversion between FPA and COSMIC was studied as an alternative simplified functional size measurement process. Our research revealed that: 1) In general it is possible to size software via simplified measurement processes with acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. The models obtained from out dataset yielded results that are similar to those reported in the literature. All simplified measurement methods that use predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to functional size tend to be quite inaccurate. In general, different methods showed different accuracy for Real-Time and non Real-Time applications. 2) It is possible to write progressively more detailed and complete UML models of user requirements that provide the data required by the simplified COSMIC methods. These models yield progressively more accurate measures of the modeled software. Initial measures are based on simple models and are obtained quickly and with little effort. As V models grow in completeness and detail, the measures increase their accuracy. Developers that use UML for requirements modeling can obtain early estimates of the applications\u2018 sizes at the beginning of the development process, when only very simple UML models have been built for the applications, and can obtain increasingly more accurate size estimates while the knowledge of the products increases and UML models are refined accordingly. 3) Both Function Point Analysis and COSMIC functional size measures appear correlated to object-oriented measures. In particular, associations with basic object- oriented measures were found: Function Points appear associated with the number of classes, the number of attributes and the number of methods; CFP appear associated with the number of attributes. This result suggests that even a very basic UML model, like a class diagram, can support size measures that appear equivalent to functional size measures (which are much harder to obtain). Actually, object-oriented measures can be obtained automatically from models, thus dramatically decreasing the measurement effort, in comparison with functional size measurement. In addition, we proposed conversion method between Function Points and COSMIC based on analytical criteria. Our research has expanded the knowledge on how to simplify the methods for measuring the functional size of the software, i.e., the measure of functional user requirements. Basides providing information immediately usable by developers, the researchalso presents examples of analysis that can be replicated by other researchers, to increase the reliability and generality of the results

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

Towards making functional size measurement easily usable in practice

Functional Size Measurement methods –like the IFPUG Function Point Analysis and COSMIC methods– are widely used to quantify the size of applications. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified measurement methods have been proposed. This research explores easily usable functional size measurement method, aiming to improve efficiency, reduce difficulty and cost, and make functional size measurement widely adopted in practice. The first stage of the research involved the study of functional size measurement methods (in particular Function Point Analysis and COSMIC), simplified methods, and measurement based on measurement-oriented models. Then, we modeled a set of applications in a measurement-oriented way, and obtained UML models suitable for functional size measurement. From these UML models we derived both functional size measures and object-oriented measures. Using these measures it was possible to: 1) Evaluate existing simplified functional size measurement methods and derive our own simplified model. 2) Explore whether simplified method can be used in various stages of modeling and evaluate their accuracy. 3) Analyze the relationship between functional size measures and object oriented measures. In addition, the conversion between FPA and COSMIC was studied as an alternative simplified functional size measurement process. Our research revealed that: 1) In general it is possible to size software via simplified measurement processes with acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. The models obtained from out dataset yielded results that are similar to those reported in the literature. All simplified measurement methods that use predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to functional size tend to be quite inaccurate. In general, different methods showed different accuracy for Real-Time and non Real-Time applications. 2) It is possible to write progressively more detailed and complete UML models of user requirements that provide the data required by the simplified COSMIC methods. These models yield progressively more accurate measures of the modeled software. Initial measures are based on simple models and are obtained quickly and with little effort. As V models grow in completeness and detail, the measures increase their accuracy. Developers that use UML for requirements modeling can obtain early estimates of the applications‘ sizes at the beginning of the development process, when only very simple UML models have been built for the applications, and can obtain increasingly more accurate size estimates while the knowledge of the products increases and UML models are refined accordingly. 3) Both Function Point Analysis and COSMIC functional size measures appear correlated to object-oriented measures. In particular, associations with basic object- oriented measures were found: Function Points appear associated with the number of classes, the number of attributes and the number of methods; CFP appear associated with the number of attributes. This result suggests that even a very basic UML model, like a class diagram, can support size measures that appear equivalent to functional size measures (which are much harder to obtain). Actually, object-oriented measures can be obtained automatically from models, thus dramatically decreasing the measurement effort, in comparison with functional size measurement. In addition, we proposed conversion method between Function Points and COSMIC based on analytical criteria. Our research has expanded the knowledge on how to simplify the methods for measuring the functional size of the software, i.e., the measure of functional user requirements. Basides providing information immediately usable by developers, the researchalso presents examples of analysis that can be replicated by other researchers, to increase the reliability and generality of the results