This article explores what constitutes an acceptable PhD, with some examples of the failings the author has noticed in theses he has been asked to examine or in his own students’ drafts.

The adoption of the most appropriate systems development methodology (SDM) for a systems developmentproject is one of the most critical decisions in systems development practice. An appropriate SDM for a sys-tems development project is adopted to avoid a misfit of a systems development methodology to the systemsdevelopment project contextual settings. An SDM misfit is caused by an SDM whose basic assumptions havebeen violated by the targeted systems development project contextual settings. A misfit may increase theeffort required for an SDM in use to fit the systems development project contextual settings. The study aimsto investigate the contingent use of SDMs as an approach to minimise SDM misfit in a systems developmentproject. The purpose of the article is to present insights on the contingent use of SDMs derived from a surveyof 155 organisations involved in systems development. The results show that both plan-driven and agile SDMclass instances are in use and that hybrid SDMs characterise most SDM implementations. The findings haveimplications for both research and practice as they unify the use of the two SDM classes under the contingentuse of SDMs approach.

All research should be guided by a process that begins with the researcher's philosophical world view and then details why the research has value, how the research was done, and why the particular approach was used. In addition, this process should be made known to the research community. In this paper we argue that theoretical or applied computing research does not normally conform to this common approach of being explicit about the research approach used but that it should do so. We adopt a pragmatic world view and use an argumentation method within a philosophical analysis design to support our position. We then adopt an accepted conceptual model of research methodology structure and use two examples to show that it can be applied across the field of computing. In addition, we propose a set of questions that could guide researchers in assessing the validity of their research. The model and the questions can be introduced to postgraduate students and developing researchers to assist them in planning and reporting their research. In addition to the practical contribution made by the model and questions, this paper adds to the debate about the place of research methodologies in Computer science research.

Technological disruption enabled by the internet revolution has resulted in an exponential increase in the growth of new technologies and the resultant technology disruptions in the workplace. Disruptive technologies are changing the rules of competition in organisations. Most chief information officers agree that there could be significant value in utilising new technology to create a competitive advantage in an agile world; however, in practice, the adoption and implementation of newer technology occur relatively slowly. There is very little research on factors influencing strategic IT decisions from a perspective of disruptive technologies. The objective of this study was to investigate critical factors that guide strategic IT decision-making in an agile business context. Using a literature review, various factors that had an influence on strategic IT decision-making in organisations were identified. The factors were extended and confirmed with data from semi-structured interviews resulting in the Framework for IT decision-making and a model for the classification of IT systems, which provide CIOs with comprehensive guidelines to make strategic IT decisions in the midst of disruptive technologies.

The rate of raw sequence production through Next-Generation Sequencing (NGS) has been growing exponentially due to improved technology and reduced costs. This has enabled researchers to answer many biological questions through “multi-omics” data analyses. Even though such data promises new insights into how biological systems function and understanding disease mechanisms, computational analyses performed on such large datasets comes with its challenges and potential pitfalls. The aim of this study was to develop a robust portable and reproducible bioinformatic pipeline for the automation of RNA sequencing (RNA-seq) data analyses. Using Nextflow as a workflow management system and Singularity for application containerisation, the nf-rnaSeqCount pipeline was developed for mapping raw RNA-seq reads to a reference genome and quantifying abundance of identified genomic features for differential gene expression analyses. The pipeline provides a quick and efficient way to obtain a matrix of read counts that can be used with tools such as DESeq2 and edgeR for differential expression analysis. Robust and flexible bioinformatic and computational pipelines for RNA-seq data analysis, from QC to sequence alignment and comparative analyses, will reduce analysis time, and increase accuracy and reproducibility of findings to promote transcriptome research.

No framework exists that can explain and predict the generalisation ability of deep neural networks in general circumstances. In fact, this question has not been answered for some of the least complicated of neural network architectures: fully-connected feedforward networks with rectified linear activations and a limited number of hidden layers. For such an architecture, we show how adding a summary layer to the network makes it more amenable to analysis, and allows us to define the conditions that are required to guarantee that a set of samples will all be classified correctly. This process does not describe the generalisation behaviour of these networks, but produces a number of metrics that are useful for probing their learning and generalisation behaviour. We support the analytical conclusions with empirical results, both to confirm that the mathematical guarantees hold in practice, and to demonstrate the use of the analysis process.

Datalog is a declarative logic programming language that uses classical logical reasoning as its basic form of reasoning. Defeasible reasoning is a form of non-classical reasoning that is able to deal with exceptions to general assertions in a formal manner. The KLM approach to defeasible reasoning is an axiomatic approach based on the concept of plausible inference. Since Datalog uses classical reasoning, it is currently not able to handle defeasible implications and exceptions. We aim to extend the expressivity of Datalog by incorporating KLM-style defeasible reasoning into classical Datalog. We present a systematic approach for extending the KLM properties and a well-known form of defeasible entailment: Rational Closure. We conclude by exploring Datalog extensions of less conservative forms of defeasible entailment: Relevant and Lexicographic Closure. We provide algorithmic definitions for these forms of defeasible entailment and prove that the definitions are LM-rational.

We present the design and implementation of an indirect messaging extension for the existing NFComms framework that provides communication between a network flow processor and host CPU. This extension addresses the bulk throughput limitations of the framework and is intended to work in conjunction with existing communication mediums. Testing of the framework extensions shows an increase in throughput performance of up to 268x that of the current direct message passing framework at the cost of increased single message latency of up to 2x. This trade-off is considered acceptable as the proposed extensions are intended for bulk data transfer only while the existing message passing functionality of the framework is preserved and can be used in situations where low latency is required for small messages.

Limited attention has been afforded to mapping the ‘landscape’ of South African computing research. Prior studies have considered singular sub-disciplines, publications, or publication types. Given the growing prominence of computing disciplines, it is necessary to identify the patterns of research production, publication, collaboration, and impact of South African computing research. This study presents a scientometric investigation in this regard. Through the analysis of data accessed from the Scopus citation enhanced bibliographic database, the investigation presents findings in relation to annual research production, institutional differences in outputs, topics, collaboration, and citation impact. While characterised by institutional differences, over the period considered, South African computing research output has increased at a greater rate than that of South African research at large. Additionally, despite accounting for a greater proportion of all outputs, conference papers account for a smaller proportion of citations relative to journal articles or book chapters. Corresponding to previous investigations, there exists a tendency towards applied computing topics in contrast to more theoretical topics. Finally, the collaboration network was shown to be particularly de-centralised with many researchers clustered around institutions. The findings are of interest to all researchers conducting computing or related research in South Africa.