Investigating the effects of personality traits on pair programming in a higher education setting through a family of experiments

Evidence from our systematic literature review revealed numerous inconsistencies in findings from the Pair Programming (PP) literature regarding the effects of personality on PP's effectiveness as a pedagogical tool. In particular: i) the effect of differing personality traits of pairs on the successful implementation of pair-programming (PP) within a higher education setting is still unclear, and ii) the personality instrument most often used had been Myers-Briggs Type Indicator (MBTI), despite being an indicator criticized by personality psychologists as unreliable in measuring an individual's personality traits. These issues motivated the research described in this paper. We conducted a series of five formal experiments (one of which was a replicated experiment), between 2009 and 2010, at the University of Auckland, to investigate the effects of personality composition on PP's effectiveness. Each experiment looked at a particular personality trait of the Five-Factor personality framework. This framework comprises five broad traits (Openness to experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism), and our experiments focused on three of these - Conscientiousness, Neuroticism, and Openness. A total of 594 undergraduate students participated as subjects. Overall, our findings for all five experiments, including the replication, showed that Conscientiousness and Neuroticism did not present a statistically significant effect upon paired students' academic performance. However, Openness played a significant role in differentiating paired students' academic performance. Participants' survey results also indicated that PP not only caused an increase in satisfaction and confidence levels but also brought enjoyment to the tutorial classes and enhanced students' motivation.

A survey of recommendation techniques based on offline data processing

Recommendations based on offline data processing has attracted increasing attention from both research communities and IT industries. The recommendation techniques could be used to explore huge volumes of data, identify the items that users probably like, translate the research results into real-world applications and so on. This paper surveys the recent progress in the research of recommendations based on offline data processing, with emphasis on new techniques (such as temporal recommendation, graph-based recommendation and trust-based recommendation), new features (such as serendipitous recommendation) and new research issues (such as tag recommendation and group recommendation). We also provide an extensive review of evaluation measurements, benchmark data sets and available open source tools. Finally, we outline some existing challenges for future research.

Automatic acceptance test case generation from essential use cases

Requirements validation is a crucial process to determine whether client-stakeholders' needs and expectations of a product are sufficiently correct and complete. Various requirements validation techniques have been used to evaluate the correctness and quality of requirements, but most of these techniques are tedious, expensive and time consuming. Accordingly, most project members are reluctant to invest their time and efforts in the requirements validation process. Moreover, automated tool supports that promote effective collaboration between the client-stakeholders and the engineers are still lacking. In this paper, we describe a novel approach that combines prototyping and test-based requirements techniques to improve the requirements validation process and promote better communication and collaboration between requirements engineers and clientstakeholders. To justify the potential of this prototype tool, we also present three types of evaluation conducted on the prototpye tool, which are the usability survey, 3-tool comparison analysis and expert reviews.

Capturing security requirements using essential use cases (EUCs)

Capturing security requirements is a complex process, but it is crucial to the success of a secure software product. Hence, requirements engineers need to have security knowledge when eliciting and analyzing the security requirements from business requirements. However, the majority of requirements engineers lack such knowledge and skills, and they face difficulties to capture and understand many security terms and issues. This results in capturing inaccurate, inconsistent and incomplete security requirements that in turn may lead to insecure software systems. In this paper, we describe a new approach of capturing security requirements using an extended Essential Use Cases (EUCs) model. This approach enhances the process of capturing and analyzing security requirements to produce accurate and complete requirements. We have evaluated our prototype tool using usability testing and assessment of the quality of our generated EUC security patterns by security engineering experts.

Convert meets KIELER: integrating advanced layout algorithms into by-example visualisations

The CONcrete Visual assistEd Transformation (CONVErT) framework provides facilities to generate reusable notations and compose them to form a wide variety of visual-isations. With an increased number of notations in large scale visualisations, it is crucial to use advanced layout algorithms to improve understandability of such complex visualisations. This showpiece paper demonstrates how advanced layout algorithms can be integrated into the notation specifications of CONVErT to generate layouts of complex visualisations.

GUITAR: An ontology-based automated requirements analysis tool

Combining goal-oriented and use case modeling has been proven to be an effective method in requirements elicitation and elaboration. However, current requirements engineering approaches generally lack reliable support for automated analysis of such modeled artifacts. To address this problem, we have developed GUITAR, a tool which delivers automated detection of incorrectness, incompleteness and inconsistency between artifacts. GUITAR is based on our goal-use case integration meta-model and ontologies of domain knowledge and semantics. GUITAR also provides comprehensive explanations for detected problems and can suggest resolution alternatives.

HorusCML: context-aware domain-specific visual languages designer

The objective behind building domain-specific visual languages (DSVLs) is to provide users with the most appropriate concepts and notations that best fit with their domain and experience. However, the existing DSVL designers do not support integrating environment and user context information when modeling, editing or viewing DSVL models at different locations, permissions, devices, etc. In this paper, we introduce HorusCML, a context-aware DSVL designer, which supports DSVL experts in integrating necessary context details within their DSVLs. The resultant DSVLs can reflect different facets, layouts, and behaviours according to context it is used in. We show a case study on developing a context-aware data flow diagram DSVL tool using HorusCML.

SecDSVL: a domain-specific visual language to support enterprise security modelling

Enterprise security management requires capturing different security and IT systems' details, analyzing and enforcing these security details, and improving employed security to meet new risks. Adopting structured models greatly helps in simplifying and organizing security specification and enforcement processes. However, existing security models are generally limited to specific security details and do not deliver a comprehensive security model. They also often do not have user-friendly notations, being complicated extensions of existing modeling languages (such as UML). In this paper, we introduce a comprehensive Security Domain Specific Visual Language (SecDSVL), which enables capturing of key security details to support enterprise systems security management process. We discuss our SecDSVL, tool support and the model-based enterprise security management approach it supports, give a usage example, and present evaluation experiments of SecDSVL.

A taxonomy and mapping of computer-based critiquing tools

Critics have emerged in recent times as a specific tool feature to support users in computer-mediated tasks. These computer-supported critics provide proactive guidelines or suggestions for improvement to designs, code, and other digital artifacts. The concept of a critic has been adopted in various domains, including medical, programming, software engineering, design sketching, and others. Critics have been shown to be an effective mechanism for providing feedback to users. We propose a new critic taxonomy based on extensive review of the critic literature. The groups and elements of our critic taxonomy are presented and explained collectively with examples, including the mapping of 13 existing critic tools, predominantly for software engineering and programming education tasks to the taxonomy. We believe this critic taxonomy will assist others in identifying, categorizing, developing, and deploying computer-supported critics in a range of domains.

Generating domain-specific visual language tools from abstract visual specifications

Domain-specific visual languages support high-level modeling for a wide range of application domains. However, building tools to support such languages is very challenging. We describe a set of key conceptual requirements for such tools and our approach to addressing these requirements, a set of visual language-based metatools. These support definition of metamodels, visual notations, views, modeling behaviors, design critics, and model transformations and provide a platform to realize target visual modeling tools. Extensions support collaborative work, human-centric tool interaction, and multiplatform deployment. We illustrate application of the metatoolset on tools developed with our approach. We describe tool developer and cognitive evaluations of our platform and our exemplar tools, and summarize key future research directions.

Using data mining for digital ink recognition: dividing text and shapes in sketched diagrams

The low accuracy rates of textshape dividers for digital ink diagrams are hindering their use in real world applications. While recognition of handwriting is well advanced and there have been many recognition approaches proposed for hand drawn sketches, there has been less attention on the division of text and drawing ink. Feature based recognition is a common approach for textshape division. However, the choice of features and algorithms are critical to the success of the recognition. We propose the use of data mining techniques to build more accurate textshape dividers. A comparative study is used to systematically identify the algorithms best suited for the specific problem. We have generated dividers using data mining with diagrams from three domains and a comprehensive ink feature library. The extensive evaluation on diagrams from six different domains has shown that our resulting dividers, using LADTree and LogitBoost, are significantly more accurate than three existing dividers.

SUMLOW: Early design-stage sketching of UML diagrams on an e-whiteboard

Most visual diagramming tools provide point-and-click construction of computer-drawn diagram elements using a conventional desktop computer and mouse. SUMLOW is a unified modelling language (UML) diagramming tool that uses an electronic whiteboard (E-whiteboard) and sketching-based user interface to support collaborative software design. SUMLOW allows designers to sketch UML constructs, mixing different UML diagram elements, diagram annotations, and hand-drawn text. A key novelty of the tool is the preservation of hand-drawn diagrams and support for manipulation of these sketches using pen-based actions. Sketched diagrams can be automatically 'formalized' into computer-recognized and -drawn UML diagrams and then exported to a third party CASE tool for further extension and use. We describe the motivation for SUMLOW, illustrate the use of the tool to sketch various UML diagram types, describe its key architecture abstractions and implementation approaches, and report on two evaluations of the toolset. We hope that our experiences will be useful for others developing sketching-based design tools or those looking to leverage pen-based interfaces in software applications.

AMC: an adaptive multi-level cache algorithm in hybrid storage systems

Hybrid storage systems that consist of flash-based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two-level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two-level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi-level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi-level cache exclusiveness and makes cache resource management more efficient. By using real-life storage traces, our evaluation shows the proposed algorithm improves hybrid multi-level cache performance and also increases the SSD lifetime compared with traditional multi-level cache replacement algorithms.