A Semantic-Agent Framework for PaaS Interoperability

Suchismita Hoare, Na Helian, and Nathan Baddoo, 'A Semantic-Agent Framework for PaaS Interoperability', in Proceedings of the The IEEE International Conference on Cloud and Big Data Computing, Toulouse, France, 18-21, July 2016. DOI: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0126 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cloud Platform as a Service (PaaS) is poised for a wider adoption by its relevant stakeholders, especially Cloud application developers. Despite this, the service model is still plagued with several adoption inhibitors, one of which is lack of interoperability between proprietary application infrastructure services of public PaaS solutions. Although there is some progress in addressing the general PaaS interoperability issue through various devised solutions focused primarily on API compatibility and platform-agnostic application design models, interoperability specific to differentiated services provided by the existing public PaaS providers and the resultant disparity owing to the offered services’ semantics has not been addressed effectively, yet. The literature indicates that this dimension of PaaS interoperability is awaiting evolution in the state-of-the-art. This paper proposes the initial system design of a PaaS interoperability (IntPaaS) framework to be developed through the integration of semantic and agent technologies to enable transparent interoperability between incompatible PaaS services. This will involve uniform description through semantic annotation of PaaS provider services utilizing the OWL-S ontology, creating a knowledgebase that enables software agents to automatically search for suitable services to support Cloud-based Greenfield application development. The rest of the paper discusses the identified research problem along with the proposed solution to address the issue.Submitted Versio

Motivators and de-motivators in software process improvement : an empirical study

Software quality problems are a concern for the software engineering community. Software Process Improvement (SPI) is the most recent and most popular approach adopted to address this problem. SPI focuses on the processes that develop software in order to deliver improvements to the product. Despite this popularity of SPI there is insufficient evidence of its successful impact on software quality. Quality problems in software continue. This has led to some concern in the industry about the effectiveness of SPI in tackling the problem of software quality. There is evidence to suggest that SPI does improve software quality. However, there is also evidence to suggest that SPI is not sufficiently supported by software practitioners. This lack of support may be one of the reasons why SPI appears to be failing at tackling the problem of software quality. In this research it is argued that this lack of support for SPI is caused by companies' inability to manage software practitioners' motivation for SPI properly. Companies may not be managing software practitioners' motivation for SPI properly because they may not understand them. There is therefore a need to better understand what software practitioners'motivations for supporting SPI are. A review of the literature suggests a set of guidelines that can improve software practitioners' support for SPI. The literature also suggests four themes that underpin software practitioners' motivation for SPI. The four themes are SPI managers' perception of the motivators and demotivators for SPI, software practitioners' motivators, software practitioners' de-motivators and the differences in software practitioners' motivators and de-motivators. The basis of this research is that exploring the four themes that underpin software practitioners' motivation for SPI improves understanding of the factors that influence support for SPI. This knowledge of the factors that influence support for SPI can then be used to validate and provide an empirical basis for the literature-suggested guidelines. Thereby improving confidence in the "-IL iidelines. The four themes underpinning software practitioners' motivation for SPI are examined through empirical studies. Findings from these studies suggest that SPI managers perceive senior managers as not supportive of SPI. They also perceive developers as not enthusiastic about SPI. The findings also suggest that the key motivators of software practitioners for SPI are visible support and commitment from senior management and empowerment of practitioners, whereas the key de-motivators are related to constraints on resources and a failure to secure practitioners' buy-in for SPI. There are also differences in what motivates and de-motivates different practitioner groups for SPI and these differences are related to the jobs that practitioners do. Finally, software practitioners have different perceptions of their role in SPI, which are related to their software development roles. This suggests that the objectives of SPI should be tailored to the software development objectives of practitioners in order to improve their support for SPI. Overall, findinas from these studies confirm most of the guidelines suggested by the t:, literature. The confirmed guidelines are offered as insight to improving support for SPI, which can in turn help to improve the impact of SPI on software quality

Models of motivation in software engineering

Motivation in software engineering is recognized as a key success factor for software projects, but although there are many papers written about motivation in software engineering, the field lacks a comprehensive overview of the area. In particular, several models of motivation have been proposed, but they either rely heavily on one particular model (the job characteristics model), or are quite disparate and difficult to combine. Using the results from our previous systematic literature review (SLR), we constructed a new model of motivation in software engineering. We then compared this new model with existing models and refined it based on this comparison. This paper summarises the SLR results, presents the important existing models found in the literature and explains the development of our new model of motivation in software engineering

Towards a Capability Maturity Framework: Adopting the universal elements of Digital Capability Maturity as an Organisational Strategy

As technology continues to evolve, there is a need for organisations to develop the ability to assess themselves and find ways to not only survive but also flourish in the dynamic economy. This paper reports part of the findings from a more extensive research work that aims to develop a Digital Capability Maturity (DCM) Framework for Higher Education Institutions (HEIs). Such a framework would allow organisations to leverage their capabilities for differential value. A systematic review was undertaken to uncover the key elements contributing to DCM, to stand as a baseline for the Maturity Framework. The objective of this paper is to report on the proposed standardisation for elements of DCM. A universal taxonomy is proposed suggesting these themes should be present in any organisational attempts to formalise digital initiatives. Furthermore, to maximise the impact of DCM on quality of output, the proposed framework must adopt the ecological systems perspective

The Jinx on the NASA software defect data sets

Background: The NASA datasets have previously been used extensively in studies of software defects. In 2013 Shepperd et al. presented an essential set of rules for removing erroneous data from the NASA datasets making this data more reliable to use. Objective: We have now found additional rules necessary for removing problematic data which were not identified by Shepperd et al. Results: In this paper, we demonstrate the level of erroneous data still present even after cleaning using Shepperd et al.'s rules and apply our new rules to remove this erroneous data. Conclusion: Even after systematic data cleaning of the NASA MDP datasets, we found new erroneous data. Data quality should always be explicitly considered by researchers before use