Software cost estimation

The paper gives an overview of the state of the art of software cost estimation (SCE). The main questions to be answered in the paper are: (1) What are the reasons for overruns of budgets and planned durations? (2) What are the prerequisites for estimating? (3) How can software development effort be estimated? (4) What can software project management expect from SCE models, how accurate are estimations which are made using these kind of models, and what are the pros and cons of cost estimation models

Fair value on commons-based intellectual property assets: Lessons of an estimation over Linux kernel.

Open source describes practices in production and development that promote access to the end product's source materials, spreading development burden amongst individuals and companies. This model has resulted in a large and efficient ecosystem and unheralded software innovation, freely available to society. Open source methods are also increasingly being applied in other fields of endeavour, such as biotechnology or cultural production. But under financial reporting framework, general volunteer activity is not reflected on financial statements. As a result, there is not value of volunteer contributions and there is also no single source for cost estimates of how much it has taken to develop an open source technology. This volunteer activity encloses not only individuals but corporations developing and contributing open source products. Standard methodology for reporting open source asset valuation is needed and must include value creation from the perspective of the different stakeholders.FLOSS, commons, accounting standards, financial reporting

Competency assessment : integrating COCOMO II and people-CMM for estimation improvement

"Human factor" is one of the most relevant and crucial aspects of software development projects management. Aiming at the performance improvement for software processes in organizations, a new model has been developed to diagnose people related processes. This new model is People-CMM and represents a complementary solution to CMM. On the other hand, existing estimation models in Software Engineering perfectly integrate those aspects related to personnel’s technical and general competence, but fail to integrate competence and performance measurement instruments when it comes to determine the precise value for each of the factors involved in the estimation process. After reviewing the already deployed initiatives and recommendations for competence measurement in the industrial environment and the most relevant estimation methods for personnel factors used in software development projects, this article presents a recommendation for the integration of each of the "human factor" related metrics in COCOMO II with the management tools proposed by People-CMM, which are widely implemented by existing commercial tools.Publicad

Towards a model for software project estimating

The use and development of software is an integral and critical part of modern industrial society. The outcomes of many software development and maintenance projects have been less than satisfactory with significant numbers being over schedule, lacking in functionality and over budget. These problems are the result of poor management of both the process and the product. One of the major problems to overcome in the management of software development projects is the ability to predict the outcomes early in the project when there are a large number of unknowns. The ability to reliably predict the outcomes in a repeatable manner requires accurate estimating techniques that are theoretically sound, practical to use, relevant to the current situation and can cope with all the project variables. Whilst a number of estimating techniques have been developed they are poor in their predictive abilities, do not to take a total project approach and are not used by practitioners. This proposal is to define a model that will build on the strengths of the current estimating techniques, account for their weaknesses and provide a framework for the development of practical techniques that encompass all aspects of a software development project

Optimization of indoor air quality towards the control of mould formation by taguchi method

The formation of mould in an indoor environment is closely related to the poor Indoor Air Quality (IAQ) which can lead to various adverse health effects such as Sick Building Syndrome (SBS) and Building Related Illness (BRI). Hence, this study was conducted to investigate the relationship between mould formation and IAQ parameters in FKAAS building. The optimization of physical IAQ parameters such as air temperature (A), relative humidity (B) and air movement (C) is conducted by Taguchi Method with L9 Orthogonal Array (OA) at 3 different levels. The response output being measured is the level of carbon dioxide (CO2) and the noise factor was time at morning and evening. The data obtained has been referred to Malaysia Standard (ICOP-2010) and ASHRAE Standard 55-2013 to verify the IAQ contamination level. From the investigation, it was found that the optimized parameters are within the acceptable range of standard and the most significant factor towards the IAQ was air movement, followed by relative humidity and air temperature. The best optimized parameters can be noted as (A:1; B:3; C:3) which is air movement at 0.195 m/s, 61% of relative humidity and air temperature of 25.77 oC. As a conclusion, Taguchi method has proven to be a powerful tool to generate robust physical parameter of IAQ, regardless of time change. In addition, the IAQ parameter definitely influence the formation of mould, and was proven by various signs of visible mould formation which indicates the unhealthy state for the occupant to stay

EEF-CAS: An Effort Estimation Framework with Customizable Attribute Selection

Existing estimation frameworks generally provide one-size-fits-all solutions that fail to produce accurate estimates in most environments. Research has shown that the accomplishment of accurate effort estimates is a long-term process that, above all, requires the extensive collection of effort estimation data by each organization. Collected data is generally characterized by a set of attributes that are believed to affect the development effort. The attributes that most affect development effort vary widely depending on the type of product being developed and the environment in which it is being developed. Thus, any new estimation framework must offer the flexibility of customizable attribute selection. Moreover, such attributes could provide the ability to incorporate empirical evidence and expert judgment into the effort estimation framework. Finally, because software is virtual and therefore intangible, the most important software metrics are notorious for being subjective according to the experience of the estimator. Consequently, a measurement and inference system that is robust to subjectivity and uncertainty must be in place. The Effort Estimation Framework with Customizable Attribute Selection (EEF-CAS) presented in this paper has been designed with the above requirements in mind. It is accompanied with four preparation process steps that allow for any organization implementing it to establish an estimation process. This estimation process facilitates data collection, framework customization to the organization’s needs, its calibration with the organization’s data, and the capability of continual improvement. The proposed framework described in this paper was validated in a real software development organization

An Empirical Analysis on Software Development Efforts Estimation in Machine Learning Perspective

The prediction of effort estimation is a vital factor in the success of any software development project. The available of expert systems for the software effort estimation supports in minimization of effort and cost for every software project at same time leads to timely completion and proper resource management of the project. This article supports software project managers and decision makers by providing the state-of-the-art empirical analysis of effort estimation methods based on machine learning approaches. In this paper ?ve machine learning techniques; polynomial linear regression, ridge regression, decision trees, support vector regression and Multilayer Perceptron (MLP) are investigated for the purpose software development effort estimation by using bench mark publicly available data sets. The empirical performance of machine learning methods for software effort estimation is investigated on seven standard data sets i.e. Albretch, Desharnais, COCOMO81, NASA, Kemerer, China and Kitchenham. Furthermore, the performance of software effort estimation approaches are evaluated statistically applying the performance metrics i.e. MMRE, PRED (25), R2-score, MMRE, Pred(25). The empirical results reveal that the decision tree-based techniques on Deshnaris, COCOMO, China and kitchenham data sets produce more adequate results in terms of all three-performance metrics. On the Albretch and nasa datasets, the ridge regression method outperformed then other techniques except pred(25) metric where decision trees performed better

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)