arXiv subjects

Stephen G. MacDonell

Publications and source records attributed to Stephen G. MacDonell.

86 records · Page 5Linked to original sources

arXiv2020

Research in Global Software Engineering: A Systematic Snapshot

This paper reports our extended analysis of the recent literature addressing global software engineering (GSE), using a new Systematic Snapshot Mapping (SSM) technique. The primary purpose of this work is to understand what issues are being addressed and how research is being carried out in GSE -- and comparatively, what work is not being conducted. We carried out the analysis in two stages. In the first stage we analyzed 275 papers published between January 2011 and June 2012, and in the second stage we augmented our analysis by considering a further 26 papers (from the 2013 International Conference on Global Software Engineering (ICGSE'13). Our results reveal that, currently, GSE studies are focused on management- and infrastructure-related factors, using principally evaluative research approaches. Most of the studies are conducted at the organizational level, mainly using methods such as interviews, surveys, field studies and case studies. The USA, India and China are major players in GSE, with USA-India collaborations being the most frequently studied, followed by USA-China. While a considerable number of GSE-related studies have been published since January 2011 they are currently quite narrowly focused, on exploratory research and explanatory theories, and the critical research paradigm has been untouched. An absence of formulative research, experimentation and simulation, and a related focus on evaluative approaches, all suggest that existing tools, methods and approaches from related fields are being tested in the GSE context, even though these may not be inherently applicable to the additional scale and complexity of GSE.

cs.SE↗

arXiv2020

A Model for Software Contexts

It is widely acknowledged by researchers and practitioners that software development methodologies are generally adapted to suit specific project contexts. Research into practices-as-implemented has been fragmented and has tended to focus either on the strength of adherence to a specific methodology or on how the efficacy of specific practices is affected by contextual factors. We submit the need for a more holistic, integrated approach to investigating context-related best practice. We propose a six-dimensional model of the problem-space, with dimensions organisational drivers (why), space and time (where), culture (who), product life-cycle stage (when), product constraints (what) and engagement constraints (how). We test our model by using it to describe and explain a reported implementation study. Our contributions are a novel approach to understanding situated software practices and a preliminary model for software contexts.

cs.SE↗

arXiv2020

Understanding Feasibility Study Approach for Packaged Software Implementation by SMEs

Software engineering often no longer involves building systems from scratch, but rather integrating functionality from existing software and components or implementing packaged software. Conventional software engineering comprises a set of influential approaches that are often considered good practice, including structured programming, and collecting a complete set of test cases. However, these approaches do not apply well for packaged software (PS) implementation; hence this phenomenon requires independent consideration. To explore PS implementation, we conducted ethnographic studies in packaged software development companies, in particular, to understand aspects of the feasibility study approach for PS implementation. From an analysis of these cases, we conclude that firstly; the analyst has more of a hybrid analyst-sales-marketing role than the analyst in traditional RE feasibility study. Secondly; the use of a live scenario software demonstration in order to convince the client to buy into the PS may lead to increased perceived feasibility and reduced resistance to PS implementation. Thirdly; the assessment criteria that are used to estimate the effort and time needed for PS implementation are new features, level of customization, software 'output', and technical needs. Fourthly; the feasibility study for PS implementation differs strongly from traditional RE as the analyst mainly considers how to deal with requests for modifications to existing functions.

cs.SE↗

arXiv2020

Experience: Quality Benchmarking of Datasets Used in Software Effort Estimation

Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location and severity of defects in code. Serious questions have been raised, however, over the quality of the data used in ESE. Data quality problems caused by noise, outliers, and incompleteness have been noted as being especially prevalent. Other quality issues, although also potentially important, have received less attention. In this study, we assess the quality of 13 datasets that have been used extensively in research on software effort estimation. The quality issues considered in this article draw on a taxonomy that we published previously based on a systematic mapping of data quality issues in ESE. Our contributions are as follows: (1) an evaluation of the "fitness for purpose" of these commonly used datasets and (2) an assessment of the utility of the taxonomy in terms of dataset benchmarking. We also propose a template that could be used to both improve the ESE data collection/submission process and to evaluate other such datasets, contributing to enhanced awareness of data quality issues in the ESE community and, in time, the availability and use of higher-quality datasets.

cs.SE↗

arXiv2020

A Systematic Mapping on the use of Visual Data Mining to Support the Conduct of Systematic Literature Reviews

A systematic literature review (SLR) is a methodology used to find and aggregate all relevant existing evidence about a specific research question of interest. Important decisions need to be made at several points in the review process, relating to search of the literature, selection of relevant primary studies and use of methods of synthesis. Visualization can support tasks that involve large collections of data, such as the studies collected, evaluated and summarized in an SLR. The objective of this paper is to present the results of a systematic mapping study (SM) conducted to collect and evaluate evidence on the use of a specific visualization technique, visual data mining (VDM), to support the SLR process. We reviewed 20 papers and our results indicate a scarcity of research on the use of VDM to help with conducting SLRs in the software engineering domain. However, most of the studies (16 of the 20 studies included in our mapping) have been conducted in the field of medicine and they revealed that the activities of data extraction and data synthesis, related to conducting the review phase of an SLR process, have more VDM support than other activities. In contrast, according to our SM, previous studies using VDM techniques with SLRs have not employed such techniques during the SLR's planning and reporting phases.

cs.SE↗

arXiv2020

Topics and Treatments in Global Software Engineering Research: A Systematic Snapshot

This study presents an analysis of the most recent literature addressing global software engineering (GSE). We examine the current state of GSE research using a new Systematic Snapshot Mapping (SSM) technique. We analysed 275 papers published between January 2011 and June 2012 in peer-reviewed conferences, journals and workshops. Our results provide a coarse-grained overview of the very recent literature addressing GSE, by classifying studies into predefined categories. We also follow and extend several prior classifications to support our synthesis of the data. Our results reveal that currently GSE studies are focused on Management and Infrastructure related factors. Most of the studies are conducted at the organizational level using methods such as interviews, surveys, field studies and case studies. We use inter-country network analysis to confirm that the USA and India are major players in GSE, with USA-India collaborations being the most frequently studied, followed by USA-China. Specific groups of countries have dominated the reported GSE project locations. In contrast, regions including Central Asia, South Asia (except India), Africa and South East Asia have not been covered in these studies. While a considerable number of GSE-related studies have been published they are currently quite narrowly focused on exploratory research and explanatory theories. The critical research paradigm has been untouched, perhaps due to a lack of criteria and principles for carrying out such research in GSE. An absence of formulative research, experimentation and simulation, and a comparative focus on evaluative approaches, all suggest that existing tools, methods and approaches from related fields are being tested in the GSE context. However, these solutions may not scale to cover GSE-related issues or may overlook factors/facets specific to GSE.

cs.SE↗

arXiv2020

Testing the Stationarity Assumption in Software Effort Estimation Datasets

Software effort estimation (SEE) models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in three software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of non-uniform weights which are subsequently employed in weighted linear regression modeling. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for datasets that exhibit underlying non-stationary processes, uniform models are more accurate than non-uniform models. In contrast, the accuracy of uniform and non-uniform models for datasets that exhibited stationary processes was essentially equivalent. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.

cs.SE↗

arXiv2020

Enhancing Data Security in the User Layer of Mobile Cloud Computing Environment: A Novel Approach

This paper reviews existing Intrusion Detection Systems (IDS) that target the Mobile Cloud Computing (MCC), Cloud Computing (CC), and Mobile Device (MD) environment. The review identifies the drawbacks in existing solutions and proposes a novel approach towards enhancing the security of the User Layer (UL) in the MCC environment. The approach named MINDPRES (Mobile- Cloud Intrusion Detection and Prevention System) combines a host-based IDS and network-based IDS using Machine Learning (ML) techniques. It applies dynamic analysis of both device resources and network traffic in order to detect malicious activities at the UL in the MCC environment. Preliminary investigations show that our approach will enhance the security of the UL in the MCC environment. Our future work will include the development and the evaluation of the proposed model across the various mobile platforms in the MCC environment.

cs.CR↗

arXiv2020

Determining Context Factors for Hybrid Development Methods with Trained Models

Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts.

cs.SE↗

arXiv2020

Time-Aware Models for Software Effort Estimation

It seems logical to assert that the dynamic nature of software engineering practice would mean that software effort estimation (SEE) modelling should take into account project start and completion dates. That is, we should build models for future projects based only on data from completed projects; and we should prefer data from recent similar projects over data from older similar projects. Research in SEE modelling generally ignores these recommendations. In this study two different model development approaches that take project timing into account are applied to two publicly available datasets and the outcomes are compared to those drawn from three baseline (non-time-aware) models. Our results indicate: that it is feasible to build accurate effort estimation models using project timing information; that the models differ from those built without considering time, in terms of the parameters included and their weightings; and that there is no statistical significance difference as to which of the two model building approaches is superior in terms of accuracy.

cs.SE↗

arXiv2014

Understanding Class-level Testability Through Dynamic Analysis

It is generally acknowledged that software testing is both challenging and time-consuming. Understanding the factors that may positively or negatively affect testing effort will point to possibilities for reducing this effort. Consequently there is a significant body of research that has investigated relationships between static code properties and testability. The work reported in this paper complements this body of research by providing an empirical evaluation of the degree of association between runtime properties and class-level testability in object-oriented (OO) systems. The motivation for the use of dynamic code properties comes from the success of such metrics in providing a more complete insight into the multiple dimensions of software quality. In particular, we investigate the potential relationships between the runtime characteristics of production code, represented by Dynamic Coupling and Key Classes, and internal class-level testability. Testability of a class is considered here at the level of unit tests and two different measures are used to characterise those unit tests. The selected measures relate to test scope and structure: one is intended to measure the unit test size, represented by test lines of code, and the other is designed to reflect the intended design, represented by the number of test cases. In this research we found that Dynamic Coupling and Key Classes have significant correlations with class-level testability measures. We therefore suggest that these properties could be used as indicators of class-level testability. These results enhance our current knowledge and should help researchers in the area to build on previous results regarding factors believed to be related to testability and testing. Our results should also benefit practitioners in future class testability planning and maintenance activities.

cs.SE↗

arXiv2014

A framework for contextual information retrieval from the WWW

Search engines are the most commonly used type of tool for finding relevant information on the Internet. However, today's search engines are far from perfect. Typical search queries are short, often one or two words, and can be ambiguous therefore returning inappropriate results. Contextual information retrieval (CIR) is a critical technique for these search engines to facilitate queries and return relevant information. Despite its importance, little progress has been made in CIR due to the difficulty of capturing and representing contextual information about users. Numerous contextual information retrieval approaches exist today, but to the best of our knowledge none of them offer a similar service to the one proposed in this paper. This paper proposes an alternative framework for contextual information retrieval from the WWW. The framework aims to improve query results (or make search results more relevant) by constructing a contextual profile based on a user's behaviour, their preferences, and a shared knowledge base, and using this information in the search engine framework to find and return relevant information.

cs.IR↗

arXiv2014

Improving web search using contextual retrieval

Contextual retrieval is a critical technique for today's search engines in terms of facilitating queries and returning relevant information. This paper reports on the development and evaluation of a system designed to tackle some of the challenges associated with contextual information retrieval from the World Wide Web (WWW). The developed system has been designed with a view to capturing both implicit and explicit user data which is used to develop a personal contextual profile. Such profiles can be shared across multiple users to create a shared contextual knowledge base. These are used to refine search queries and improve both the search results for a user as well as their search experience. An empirical study has been undertaken to evaluate the system against a number of hypotheses. In this paper, results related to one are presented that support the claim that users can find information more readily using the contextual search system.

cs.IR↗

arXiv2014

An automatic architecture reconstruction and refactoring framework

A variety of sources have noted that a substantial proportion of non trivial software systems fail due to unhindered architectural erosion. This design deterioration leads to low maintainability, poor testability and reduced development speed. The erosion of software systems is often caused by inadequate understanding, documentation and maintenance of the desired implementation architecture. If the desired architecture is lost or the deterioration is advanced, the reconstruction of the desired architecture and the realignment of this desired architecture with the physical architecture both require substantial manual analysis and implementation effort. This paper describes the initial development of a framework for automatic software architecture reconstruction and source code migration. This framework offers the potential to reconstruct the conceptual architecture of software systems and to automatically migrate the physical architecture of a software system toward a conceptual architecture model. The approach is implemented within a proof of concept prototype which is able to analyze java system and reconstruct a conceptual architecture for these systems as well as to refactor the system towards a conceptual architecture.

cs.SE↗