Developing Educational Software: a professional tool perspective

The selection, and use of educational software and its impact in schools are still controversial issues. In this paper we present an alternative conceptualisation of educational software based on considering the software as an instrument for teachers’ professional performance. We review previous work in the areas of the design, development and evaluation of educational software and of the process of educational innovation. The review of these four areas converges to demonstrate the need for knowing and considering the context of use of educational software and for understanding users' perspectives about its roles and possibilities and hence supports a consideration a perspective on educational software which sees it as a professional tool for teachers performance of their teaching role

The impact of Software Process Maturity on Software Project Performance: The Contingent Role of Software Development Risk

Despite growing efforts to improve software development processes, recurring concerns about software project performance remain largely present. The rate of software development project failure rate has been routinely documented in information systems (IS) research (Wallace, 2004; El-Masry and Rivard, 2010). The management of software development projects is often marked by inadequate planning, a poor grasp of the overall development process, and no clear management framework, even as the focus in software development shifts from a technology perspective to a more process-centric view (Slaughter, 2006). To address such concerns few CMM-based studies have examined the benefits and direct impact of software process maturity on software project performance but with mixed results. The present paper attempts to systematically examine the contingent role of software development risk on the impact of software process maturity level on software project performance. Guided by risk-based perspective in Software Engineering and CMM-based framework, an exploratory model was developed and tested. The premise of this paper is that software development risk plays a contingent role in the relationship between software process maturity and software project performance. Drawing on a sample of 107 organizations that have undergone official CMM appraisals, the results of partial least squares analysis of the data reveal initial evidence that (1) a positive effect of software process maturity level on software project performance while underscoring the negative effect of software development risk on software project performance, and (2) more importantly, the findings show that software development risk plays a contingent role software process maturity level on software project performance. For researchers, the integration of software development risk can provide a much needed linkage in the three fundamental constructs of CMM. From a managerial perspective, in order to foster a better software project performance, IS project leaders and managers should strongly emphasize devising effective software development risk assessment since a variation of this construct’s level may strengthen or weaken the relationship between software development process maturity and software project performance

Does Critical Success Factors of KM Mediate the KS-Driven Performance in the Software Sector of Pakistan?

This research tends to investigate role of KS practices on overall performance of software sector using critical success factors of KM (i.e. KM process and infrastructure capabilities) as intermediate measures. In this regard, a survey method using the adapted instrument to draw the inference from data collected from software developers.  The parallel multiple mediation model proposed and tested by using Process Macro. The findings of study reveal that KS practices have a significant and positive effect on the overall performance in terms of operational excellence, financial achievement, customer intimacy, product leadership. The results indicate that all the constructs of KM process and infrastructure capabilities partially mediates the relationship between KS practices and performance of software sector. Hence, findings of study support all the suggested hypotheses and draws the inference that KM process and infrastructure capabilities support the theoretical prisms of KBV initiatives

The Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review

Large-scale simulations play a central role in science and the industry. Several challenges occur when building simulation software, because simulations require complex software developed in a dynamic construction process. That is why simulation software engineering (SSE) is emerging lately as a research focus. The dichotomous trade-off between scalability and efficiency (SE) on the one hand and maintainability and portability (MP) on the other hand is one of the core challenges. We report on the SE/MP trade-off in the context of an ongoing systematic literature review (SLR). After characterizing the issue of the SE/MP trade-off using two examples from our own research, we (1) review the 33 identified articles that assess the trade-off, (2) summarize the proposed solutions for the trade-off, and (3) discuss the findings for SSE and future work. Overall, we see evidence for the SE/MP trade-off and first solution approaches. However, a strong empirical foundation has yet to be established; general quantitative metrics and methods supporting software developers in addressing the trade-off have to be developed. We foresee considerable future work in SSE across scientific communities.Comment: 9 pages, 2 figures. Accepted for presentation at the Fourth International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering (SEHPCCSE 2016

Ensemble learning for software fault prediction problem with imbalanced data

Fault prediction problem has a crucial role in the software development process because it contributes to reducing defects and assisting the testing process towards fault-free software components. Therefore, there are a lot of efforts aiming to address this type of issues, in which static code characteristics are usually adopted to construct fault classification models.  One of the challenging problems influencing the performance of predictive classifiers is the high imbalance among patterns belonging to different classes. This paper aims to integrate the sampling techniques and common classification techniques to form a useful ensemble model for the software defect prediction problem. The empirical results conducted on the benchmark datasets of software projects have shown the promising performance of our proposal in comparison with individual classifiers

Performance requirements verification during software systems development

Requirements verification refers to the assurance that the implemented system reflects the specified requirements. Requirement verification is a process that continues through the life cycle of the software system. When the software crisis hit in 1960, a great deal of attention was placed on the verification of functional requirements, which were considered to be of crucial importance. Over the last decade, researchers have addressed the importance of integrating non-functional requirement in the verification process. An important non-functional requirement for software is performance. Performance requirement verification is known as Software Performance Evaluation. This thesis will look at performance evaluation of software systems. The performance evaluation of software systems is a hugely valuable task, especially in the early stages of a software project development. Many methods for integrating performance analysis into the software development process have been proposed. These methodologies work by utilising the software architectural models known in the software engineering field by transforming these into performance models, which can be analysed to gain the expected performance characteristics of the projected system. This thesis aims to bridge the knowledge gap between performance and software engineering domains by introducing semi-automated transformation methodologies. These are designed to be generic in order for them to be integrated into any software engineering development process. The goal of these methodologies is to provide performance related design guidance during the system development. This thesis introduces two model transformation methodologies. These are the improved state marking methodology and the UML-EQN methodology. It will also introduce the UML-JMT tool which was built to realise the UML-EQN methodology. With the help of automatic design models to performance model algorithms introduced in the UML-EQN methodology, a software engineer with basic knowledge of performance modelling paradigm can conduct a performance study on a software system design. This was proved in a qualitative study where the methodology and the tool deploying this methodology were tested by software engineers with varying levels of background, experience and from different sectors of the software development industry. The study results showed an acceptance for this methodology and the UML-JMT tool. As performance verification is a part of any software engineering methodology, we have to define frame works that would deploy performance requirements validation in the context of software engineering. Agile development paradigm was the result of changes in the overall environment of the IT and business worlds. These techniques are based on iterative development, where requirements, designs and developed programmes evolve continually. At present, the majority of literature discussing the role of requirements engineering in agile development processes seems to indicate that non-functional requirements verification is an unchartered territory. CPASA (Continuous Performance Assessment of Software Architecture) was designed to work in software projects where the performance can be affected by changes in the requirements and matches the main practices of agile modelling and development. The UML-JMT tool was designed to deploy the CPASA Performance evaluation tests

Performance Modeling and Analysis of Software Architectures Specified Through Graph Transformations

Software architecture plays an important role in the success of modern, large and distributed software systems. For many of the software systems -- especially safety-critical ones -- it is important to specify their architectures using formal modeling notations. In this case, it is possible to assess different functional and non-functional properties on the designed models. Graph Transformation System (GTS) is a formal yet understandable language which is suitable for architectural modeling. Most of the existing works done on architectural modeling and analysis by GTS are concentrated on functional aspects, while for many systems it is crucial to consider non-functional aspects for modeling and analysis at the architectural level. In this paper, we present an approach to performance analysis of software architectures specified through GTS. To do so, we first enrich the existing architectural style -- specified through GTS - with performance information. Then, the performance models are generated in PEPA (Performance Evaluation Process Algebra) -- a formal language based on the stochastic process algebra -- using the enriched GTS models. Finally, we analyze different features like throughput, utilization of different software components, etc. on the generated performance models. All the main concepts are illustrated through a case study

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects

Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students' skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development

Toward an Effective Automated Tracing Process

Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry

Enterprise Systems Adoption and Firm Performance in Europe: The Role of Innovation

Despite the ubiquitous proliferation and importance of Enterprise Systems (ES), little research exists on their post-implementation impact on firm performance, especially in Europe. This paper provides representative, large-sample evidence on the differential effects of different ES types on performance of European enterprises. It also highlights the mediating role of innovation in the process of value creation from ES investments. Empirical data on the adoption of Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Customer Relationship Management (CRM), Knowledge Management System (KMS), and Document Management System (DMS) is used to investigate the effects on product and process innovation, revenue, productivity and market share growth, and profitability. The data covers 29 sectors in 29 countries over a 5-year period. The results show that all ES categories significantly increase the likelihood of product and process innovation. Most of ES categories affect revenue, productivity and market share growth positively. Particularly, more domainspecific and simpler system types lead to stronger positive effects. ERP systems decrease the profitability likelihood of the firm, whereas other ES categories do not show any significant effect. The findings also imply that innovation acts as a full or partial mediator in the process of value creation of ES implementations. The direct effect of enterprise software on firm performance disappears or significantly diminishes when the indirect effects through product and process innovation are explicitly accounted for. The paper highlights future areas of research.Enterprise Systems; ERP; SCM; CRM; KMS; DMS; IT Adoption; Post-implementation Phase; IT Business Value; Innovation; Firm Performance; Europe