Influence Factors for the Choice of a Software Development Methodology

The success rate of software development projects can be increased by using a methodology that is adequate for the specific characteristics of those projects. Over time a wide range of software development methodologies has been elaborated, therefore choosing one of them is not an easy task. Our research reviews the main categories of development methodologies and then focuses, for a detailed study, on three of them: Rational Unified Process (RUP), Rapid Application Development (RAD) and Extreme Programming (XP). For each methodology it is presented the structure of software life cycle, there are identified the situations in which the methodology can be used successfully and the situations in which it tends to fail. Based on the literature review of software development methodologies and on a series of surveys, published by different researchers, exploring the state of practices in this field, we have identified a number of factors that influence the decision of choosing the most adequate development methodology for a specific project. The methodologies that are subject of this study are evaluated in relation to these factors to find out which development methodology is the most adequate depending on the level of the factors for a specific project. The results of our research are useful for the developers by helping them to identify what software development methodology can be used with success for a specific project.Software development methodology, Rational Unified Process, Rapid Application Development, Extreme Programming, choosing the adequate methodology

The Knowledge-Based Software Assistant: Beyond CASE

This paper will outline the similarities and differences between two paradigms of software development. Both support the whole software life cycle and provide automation for most of the software development process, but have different approaches. The CASE approach is based on a set of tools linked by a central data repository. This tool-based approach is data driven and views software development as a series of sequential steps, each resulting in a product. The Knowledge-Based Software Assistant (KBSA) approach, a radical departure from existing software development practices, is knowledge driven and centers around a formalized software development process. KBSA views software development as an incremental, iterative, and evolutionary process with development occurring at the specification level

Process Models and Distribution of Work in Offshoring Application Software Development

Common process models for the development of application software (AS) are examined as to how well they are suited for offshoring projects. The need for communication and interaction among onsite and offshore project stakeholders is identified as a critical success factor. Process models used by organizations providing offshoring services are discussed, and a generalized offshoring life cycle model is developed. A specific focus is set on the distribution of work between the organization that outsources AS development and the offshore organization that carries out the major share of the development work. Problems and challenges that have to be faced, making offshoring a difficult task, are discussed. --

llc: a collection of R functions for fitting a class of Lee-Carter mortality models using iterative fitting algorithms

We implement a specialised iterative regression methodology in R for the analysis of age-period mortality data based on a class of generalised Lee-Carter (LC) type modelling structures. The LC-based modelling frameworks is viewed in the current literature as among the most efficient and transparent methods of modelling and projecting mortality improvements. Thus, we make use of the modelling approach discussed in Renshaw and Haberman (2006), which extends the basic LC model and proposes to make use of a tailored iterative process to generate parameter estimates based on Poisson likelihood. Furthermore, building on this methodology we develop and implement a stratified LC model for the measurement of the additive effect on the log scale of an explanatory factor (other than age and time). This modelling methodology is implemented in a publically available collection of programming functions that facilitate both the preparation of mortality data and the fitting and analysis of the given log-linear modelling structures. Also, the package incorporates methods to produce forecasts of future mortality rates and to compute the corresponding future life expectancy