Towards a design theoretic characterisation of software development process models

Effective assessment, comparison, selection and adaptation of software development processes remain an acute problem in Software Engineering practice. The quest for a unified theory which might serve this purpose is ongoing. Objective: To take a first step towards such a theory, with focus on characterising and comparing features of software development process models. Method: We consider a theory of design as problem solving and investigate how it can be applied to characterise and explicate specific process features in well known process models from the literature. The intention is to characterise emerging trade-offs between resource expenditure and risk mitigation, which result from the interplay between process efforts into problem and solution exploration vs. stakeholder validation. The analysis, at this point performed, is purely qualitative, and the treatment of resource expenditure and risk quite abstract. Results: We provide an initial characterisation and comparison of features found in a wide range of process models from the literature, within a design theoretic framework using a single building block -- the Problem Oriented Engineering (POE) Process Pattern -- that allows the characterisation of information flow, the relationship between actors, resource usage and developmental risk. Conclusions: The initial characterisation identifies repeated structure in diverse processes, which allows basic process comparison across models. The interpretations are modular, allowing the possibility of relationships between different process models to be explored. As such, the theory allows for a unified means to characterise and compare systematically key features of different process models. In being of an exploratory nature, the work has a number of limitations, which should be addressed by further research

A Non-Parametric Comparison between Advances Software Engineering Process Model

Software development process provides detailed guideline for development testing and maintenance of software products. It deals with the risks associated withsoftware development and a road map to manage its complexities. In other words, software development processes are considered as optimized solution specific to any particular software product development. There are many software process models available in literature. This research performs a non-parametric comparison between formal process model, agile process model and agent based process model to aid software community in developing quality software product

Software process quality models: a comparative evaluation

Numerous software processes are implemented by software organisations in the production and maintenance of software products. Varying levels of success are observed in their execution, as processes vary in content and quality. A number of quality models for software processes have been published, each of which is intended to encompass the totality of quality factors and issues relevant to a specific notion of process quality. These quality models may be used to develop a new process, measure the quality of existing processes, or guide improvement of existing processes. It is therefore desirable that mechanisms exist to select the model of highest intrinsic quality and greatest relevance. In this thesis, mechanisms are proposed for the comparative evaluation of software process quality models. Case studies are performed in which existing software process quality models are applied to existing software processes. Case study results are used in empirical evaluation of models to augment theoretical evaluation results. Specific recommendations are made for selection of models against typical selection criteria. Assessment is performed of the assessment procedures against defined success criteria. Theoretical evaluation procedures are developed to measure process quality models against defined quality criteria. Measurements are performed of conformance of models to the requirements set for an ideal process quality model, and the relevance of model content to defined stakeholders in software processes. Comparison is also made of the scope and size of models. Empirical evaluation procedures are developed to assess model performance in the context of application to real software processes. These procedures assess the extent to which the results of process measurement using process quality models are observed to differ, and hence the importance of selecting one model in preference to others. Measurement is also performed of the extent of difference in the software processes evaluated in the case studies

GPfit: An R package for Gaussian Process Model Fitting using a New Optimization Algorithm

Gaussian process (GP) models are commonly used statistical metamodels for emulating expensive computer simulators. Fitting a GP model can be numerically unstable if any pair of design points in the input space are close together. Ranjan, Haynes, and Karsten (2011) proposed a computationally stable approach for fitting GP models to deterministic computer simulators. They used a genetic algorithm based approach that is robust but computationally intensive for maximizing the likelihood. This paper implements a slightly modified version of the model proposed by Ranjan et al. (2011), as the new R package GPfit. A novel parameterization of the spatial correlation function and a new multi-start gradient based optimization algorithm yield optimization that is robust and typically faster than the genetic algorithm based approach. We present two examples with R codes to illustrate the usage of the main functions in GPfit. Several test functions are used for performance comparison with a popular R package mlegp. GPfit is a free software and distributed under the general public license, as part of the R software project (R Development Core Team 2012).Comment: 20 pages, 17 image

Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction

Risk Management Method in IT Project: a Review

In the development of a software, there are several aspects that must be taken to ensure that the process can produce a useful product and make a profit. This article clarified some of the methods of risk management exist. There was two techniques to determine the risks used in this study, those were Metrics of Process Structure and Referential Model or could be referred as the Comparison to the Referential Model technique. That technique will produce Software Process Meta Model, Model of Risk Management, and Manage Risks in Project models. Those models were used to help managers in mapping the risks of the project

Multi-disciplinary shape optimization of an entry capsule integrated with custom neural network approximation and multi-delity approach

This paper describes a new integrated approach for the multi-disciplinary optimization of a entry capsule’s shape. Aerothermodynamics, Flight Mechanics and Thermal Protection System behaviour of a reference spaceship when crossing Martian atmosphere are considered, and several analytical, semi-empirical and numerical models are used. The multi-objective and multi-disciplinary optimization process implemented in Isight software environment allows finding a Pareto front of best shapes. The optimization process is integrated with a set of artificial neural networks, trained and updated by a multi-fidelity evolution control approach, to approximate the objective and constraint functions. Results obtained by means of the integrated approach with neural networks approximators are described and compared to the results obtained by a different optimization process, not using the approximators. The comparison highlights advantages and possible drawbacks of the proposed method, mainly in terms of calls to the true model and precision of the obtained Pareto front

Using Counts as Heuristics for the Analysis of Static Models

The upstream activities of software development are often viewed as both the most important, in terms of cost, and the yet the least understood, and most problematic, particularly in terms of satisfying customer requirements. Business process modelling is one solution that is being increasingly used in conjunction with traditional software development, often feeding in to requirements and analysis activities. In addition, research in Systems Engineering for Business Process Change, highlights the importance of modelling business processes in evolving and maintaining the legacy systems that support those processes. However, the major use of business process modelling, is to attempt to restructure the business process, in order to improve some given aspect, e.g., cost or time. This restructuring may be seen either as separate activity or as a pre-cursor to the development of systems to support the new or improved process. Hence, the analysis of these business models is vital to the improvement of the process, and as a consequence to the development of supporting software systems. Supporting this analysis is the focus of this paper. Business processes are typically described with static (diagrammatic) models. This paper proposes the use of measures (counts) to aid analysis and comparison of these static process descriptions. The proposition is illustrated by showing how measures can be applied to a commonly used process-modelling notation, Role Activity Diagrams (RADs). Heuristics for RADs are described and measures suggested which support those heuristics. An example process is used to show how a coupling measure can be used to highlight features in RADs useful to the process modeller. To fully illustrate the proposition the paper describes and applies a framework for the theoretical validation of the coupling measure. An empirical evaluation follows. This is illustrated by two case studies; the first based on the bidding process of a large telecommunications systems supplier, and the second a study of ten prototyping processes across a number of organisations. These studies found that roles of the same type exhibited similar levels of coupling across processes. Where roles did not adhere to tentative threshold values, further investigation revealed unusual circumstances or hidden behaviour. Notably, study of the prototyping roles, which exhibited the greatest variation in coupling, found that coupling was highly correlated with the size of the development team. This suggests that prototyping in large projects had a different process to that for small projects, using more mechanisms for communication. Hence, the empirical studies support the view that counts (measures) may be useful in the analysis of static process models