People often utilise online media (e.g., Facebook, Reddit) as a platform to express their psychological distress and seek support. State-of-the-art NLP techniques demonstrate strong potential to automatically detect mental health issues from text. Research suggests that mental health issues are reflected in emotions (e.g., sadness) indicated in a person’s choice of language. Therefore, we developed a novel emotion-annotated mental health corpus (EmoMent),consisting of 2802 Facebook posts (14845 sentences) extracted from two South Asian countries - Sri Lanka and India. Three clinical psychology postgraduates were involved in annotating these posts into eight categories, including ‘mental illness’ (e.g., depression) and emotions (e.g., ‘sadness’, ‘anger’). EmoMent corpus achieved ‘very good’ inter-annotator agreement of 98.3% (i.e. % with two or more agreement) and Fleiss’ Kappa of 0.82. Our RoBERTa based models achieved an F1 score of 0.76 and a macro-averaged F1 score of 0.77 for the first task (i.e. predicting a mental health condition from a post) and the second task (i.e. extent of association of relevant posts with the categories defined in our taxonomy), respectively.

Increased opportunities for online learning, including growth in Massive Open Online Courses (MOOCS), are changing our education environments, increasing access and flexibility in how students engage with education. However, there are still many questions regarding how we engage with students effectively in these environments, in particular through assessment.

Computer Science (CS) is a new subject area for many K-12 teachers around the world, requiring new disciplinary knowledge and skills. Teacher social-behavioral factors (e.g. self-esteem) have been found to impact learning and teaching, and a key part of CS curriculum implementation will need to ensure teachers feel confident to deliver CS. However, studies about CS teacher self-esteem are lacking. This paper presents an analysis of publicly available data (n=219) from a pilot study using a Teacher CS Self-Esteem scale. Analysis revealed significant differences, including 1) females reported significantly lower CS self-esteem than males, 2) primary teachers reported lower levels of CS self-esteem than secondary teachers, 3) those with no CS teaching experience reported significantly lower CS self-esteem, 4) teachers with 0-3 years experience had a negative CS self-esteem, but after four years, teachers had a positive CS self-esteem, and 5) teachers who lived further from metropolitan areas and in some countries reported lower CS self-esteem. These initial findings suggest a pressing need for future research to look further into teacher CS self-esteem to inform teacher CS professional development.

The 32 chapters of the 2019 Cambridge Handbook of Computing Education Research synthesize the existing research in computing education and propose new directions for future research. An author from each chapter will summarize their chapter with auto-advancing slides. Attendees will be introduced to the breadth of content in the new handbook and can identify chapters of interest. This fits uniquely as a special session, and will likely be informative, inspiring, and overwhelming.

Performance anomaly detection is crucial for long running, large scale distributed systems. However, existing works focus on the detection of specific types of anomalies, rely on historical failure data, and cannot adapt to changes in system behavior at run time. In this work, we propose an adaptive framework for the detection and identification of complex anomalous behaviors, such as deadlocks and livelocks, in distributed systems without historical failure data. Our framework employs a two-step process involving two online SVM classifiers on periodically collected system metrics to identify at run time normal and anomalous behaviors such as deadlock, livelock, unwanted synchronization, and memory leaks. Our approach achieves over 0.70 F-score in detecting previously unseen anomalies and 0.78 F-score in identifying the type of known anomalies with a short delay after the anomalies appear, and with minimal expert intervention. Our experimental analysis uses system execution traces from our in-house distributed system with varied behaviors and a dataset by Yahoo!, and shows the benefits of our approach as well as future research challenges.

This study explores how a well-formed programming assignment can assist students in understanding a problem. To determine whether the presentation of a programming assignment might help CS1 students better understand the problem, we perform a comparable replication study using Soloway's Rainfall Problem. This study integrates scaffolding treatments from prior studies using the Rainfall Problem into a theoretical assignment design pattern, to form a new presentation context. A survey was used to gain insight into how students perceived the presentation helped them understand the problem and potentially helped them solve the programming tasks. Results from the study show plan composition and natural language translation a challenge, but students appreciate the additional guidance from the assignment presentation in the form of hints and bullet points formatting to identify tasks. From the results, we are considering these scaffolding treatments and assignment presentation for future CS1 programming assignments.

Best practice for building teacher capacity in subject domains requires strong links to be made between the curriculum content, pedagogy and the effective use of teaching resources. Until recently primary school computing education has largely focused on developing digital literacy and the effective use Information Communication Technologies (ICTs) for learning and teaching. However, the introduction of new Computer Science (CS) curricula worldwide raises a significant need for finding ways to build our understanding of effective learning and teaching and how to build teacher capacity.

There has been a growing interest and increase in work shared about national K-12 Computer Science Education (CSED) curriculum and implementation efforts around the world. Much of this work focuses on curriculum analysis, country reports, experience reports and case studies. The K-12 CSED community would benefit from an international strategic effort to compare, contrast and monitor K-12 CSED over time, across multiple countries and regions, to understand pedagogy, practice, resources and experiences from the perspective of teachers working in classrooms. Furthermore, there is a need for validated and robust instruments that can support comparable investigations into the current state of K-12 CSED in schools around the world. Through a collaborative effort, this Working Group will develop a validated teacher survey instrument and collect data about CSED implementation and practice in K-12 classrooms. The authors will pilot the survey with K-12 teacher cohorts and analyse, compare and contrast survey findings across countries.

Questioning is a learning activity that can promote the use of critical thinking skills, where thinking processes are required to answer a posed question. In this paper, we map instructional question types to Bloom's Taxonomy--a classification of critical thinking skills required for cognition--to form a framework for educators to construct learning activities through questioning. The preliminary instructional framework is applied to a question activity within a blended CS1 learning environment to support students in better understanding on how to solve a programming assignment. Our results show students meeting the desired Bloom's cognitive level when answering the question activity. Future research opportunities are presented to test the framework for upper-division CS courses and further explore the framework as an intervention for programming assignments.

Parsons Problems is an effective Introductory Programming teaching tool that allows students to arrange code to form a functional program, reducing their cognitive load so they can focus on programming concepts. This demonstration shows how Parsons Problems can be used as a pedagogical strategy in a Massive Open Online Course (MOOC) for introductory programming. A MOOC is a learning platform, where compilers and IDEs have already been embedded for CS students to immerse themselves in a single learning environment. The Parsons Problems package, js-parsons, available at https://github.com/js-parsons/js-parsons has been successfully integrated into the University of Adelaide's edX's MOOC platform as a component that can potentially be made available for other edX platforms. The demonstration presents the educators' interface to add new questions and receive students' results, along with students' perspective, including subgoals. The demonstration prototypes new feedback models when students encounter Parsons Problems errors, opening discussion up to the audience for opinions and input. This demonstration is intended for anyone wanting to know how to use Parsons Problems within MOOCs as a pedagogical approach; those seeking to incorporate MOOCs into their coursework with focused exercises; and those wanting to contribute to future Parsons Problems enhancements. Laptops are recommended, since participants will have the hands-on opportunity to evaluate the package during the demonstration.

Abstract Security methodologies represent systematic approaches for introducing security attributes into a system throughout the development lifecycle. While isolated attempts have been made to demonstrate the value of particular security methodologies, the “quality” of security methodologies, as such, has never been given due consideration; indeed, it has never been studied as a self‐standing topic. The literature therefore entirely lacks supportive artifacts that can provide a basis for assessing, and hence for improving, a security methodology's quality. In this paper, we fill the aforementioned gap by proposing a comprehensive quality framework and accompanying process, within the context of an existing approach to engineering security methodologies, which can be used for both (bottom‐up) quality assessment and (top‐down) quality improvement. The main framework elements can be extended and customized to allow an essentially arbitrary range of methodology features to be considered, thus forming a basis for flexible, fine‐grained quality control. We demonstrate the bottom‐up application of the latter framework and process on three real‐life security methodologies for distributed systems, taken as case studies. Based on the assessment results, we subsequently show in detail (for one) and briefly discuss (for the remaining set) how the case study methodologies can be re‐engineered to improve their quality.

Due to the rapidly changing nature of today's work environment, software engineering (SE) students are required to have self-regulated learning (SRL) and problem solving skills. Previous research suggests that training students in the use of domain-specific cognitive strategies and using scaffolded instruction for strategy training improves students' SRL and problem solving task performance. In order to identify SE-specific cognitive strategies, we conducted a survey of advanced-level SE students. We then conducted a pre-test and post-test experiment with one control and two treatment groups, to analyze the effectiveness of identified strategies in improving students' task performance. The control group was not exposed to any strategies, while one treatment group was instructed verbally in the use of strategies and the other was trained using a newly developed scaffolded strategy training module. The results of the experiment demonstrate significant improvement in post-test task performance for both treatment groups, with a further increase in performance for those undertaking the training module.

Safeguarding academic integrity is an issue of concern to all computing academics due to high and rising levels of plagiarism and other cheating in computing courses. There have been many studies of the cheating and plagiarism practices of computing students and the factors that can influence these practices, and a variety of strategies for reducing cheating have been proposed. This national study of first-year computing programs provides insights into what strategies computing academics use to discourage or prevent their students from cheating. Having interviewed 30 academics from 25 universities we found 21 different types of strategy, which we classified into five themes: education; discouraging cheating; making cheating difficult; and empowerment. We also found that academics often employ strategies across all of these themes.

Lecture videos are amongst the most commonly used instructional methods in present Massive Open Online Courses (MOOCs). As the main form of instruction, students' engagement behaviour with MOOC videos directly impacts the students' success or failure. This research focuses on an in-depth analysis of 1.5 million video interactions (e.g. pause, seek video) of a Programming MOOC. Our video-by-video analysis explores the rationale behind the time-wise variation of video interactions. We aim to analyse discourse features (e.g. syntactic simplicity of text, and speaking rate) and their correlation with the video interaction patterns. This paper presents preliminary results and educational video design implications.

It can be difficult for educators with limited resources to decide which queries need immediate attention when a high volume of questions arises on the discussion forum. This becomes increasingly complex as the educator aims to obtain the best outcome across all threads in a timely fashion. Existing approaches to automated forum analysis provide a useful grouping of messages and identify common discussions, but require additional attention towards effective intervention. Research has shown that the timing of messages relative to associated deadlines is a key indicator of priority. In this paper, we propose the formal representation of events within a discussion forum to facilitate the definition of potential and existing intervention strategies. We enhance forum events with information about teaching activities, such as assignment deadlines, and discuss intervention strategies.

A number of institutions and organisations provide online or face-to-face professional learning as part of outreach initiatives to increase skill levels and support for teachers in K-12 Computing education. With a number of countries introducing new K-12 Computer Science curricula around the globe, this provides a prime opportunity for the Computer Science education community to transform and develop models of teacher professional learning that address teachers' needs at-scale. This paper explores the theoretical underpinnings of a community-based professional learning MOOC for Australian teachers for K-6 Computer Science. This paper reflects on data collected from three offerings of the MOOC, presented in light of the theory and design considerations. This paper provides valuable insights of the design of community-centric MOOCs, and acts as a guide for the construction of online professional learning opportunities for Computer Science educators.

This special session is based around key findings of a Barbara Cail STEM Fellowship that aims to inform key stakeholders about international best practices for broadening participation and diversity in computer science. This special session provides opportunities for the audience to contribute to this research discussion and to analyse and develop strategies for their own unique contexts, in a facilitated approach using the benchmark framework and key findings.

Significant advances have been made in the learning and teaching of Introductory Programming, including the integration of active and contextualised learning pedagogy. However, Massively Open Online Courses (MOOCs), where Computer Science and, more specifically, introductory programming courses dominate, do not typically adopt such pedagogies or lessons learned from more traditional learning environments. Moreover, the improvement of learning within the MOOC context in terms of discipline-specific pedagogy, and the improvement of student learning outcomes and processes have not been studied in depth.

This study proposes a standardised open framework to automatically generate and label discussion topics from Massive Open Online Courses (MOOCs). The proposed framework expects to overcome the issues experienced by MOOC participants and teaching staff in locating and navigating their information needs effectively. We analysed two MOOCs -- Machine Learning and Statistics: Making Sense of Data offered during 2013 and obtained statistically significant results for automated topic labeling. However, more experiments with additional MOOCs from different MOOC platforms are necessary to generalise our findings.

Although teamwork has been identified as an essential skill for Computer Science (CS) graduates, these skills are identified as lacking by industry employers, which suggests a need for more proactive measures to teach and assess teamwork. In one CS course, students worked in teams to create a wiki solution to problem-based questions. Through a case-study approach, we test a developed teamwork framework, using manual content analysis and sentiment analysis, to determine if the framework can provide insight into students’ teamwork behavior and to determine if the wiki task encouraged students to collaborate, share knowledge, and self-adopt teamwork roles. Analysis revealed the identification of both active and cohesive teams, disengaged students, and particular roles and behaviors that were lacking. Furthermore, sentiment analysis revealed that teams moved through positive and negative emotions over the course of developing their solution, toward satisfaction. The findings demonstrate the value of the detailed analysis of online teamwork. However, we propose the need for automated measures that provide real-time feedback to assist educators in the fair and efficient assessment of teamwork. We present a prototype system and recommendations, based on our analysis, for automated teamwork analysis tools.

The development of discipline-specific cognitive and meta-cognitive skills is fundamental to the successful mastery of software development skills and processes. This development happens over time and is influenced by many factors, however its understanding by teachers is crucial in order to develop activities and materials to transform students from novice to expert software engineers. In this paper, we analyse the evolution of learning strategies of novice, first year students, to expert, final year students. We analyse reflections on software development processes from students in an introductory software development course, and compare them to those of final year students, in a distributed systems development course. Our study shows that computer science - specific strategies evolve as expected, with the majority of final year students including design before coding in their software development process, but that several areas still require scaffolding activities to assist in learning development.

Many software development organisations today adopt global software engineering (GSE) and agile models, requiring software engineers to collaborate and develop software in flexible, distributed, online teams. However, many employers have expressed concern that graduates lack teamwork skills and one of the most commonly occurring problems with GSE models are issues with project management. Team managers and educators often oversee a number of teams and the large corpus of data, in combination with agile models, make it difficult to efficiently assess factors such as team role distribution and emotional climate. Current methods and tools for monitoring software engineering (SE) teamwork in both industry and education settings typically focus on member contributions, reflection, or product outcomes, which are limited in terms of real-time feedback and accurate behavioural analysis. We have created a dashboard that extracts and communicates team role distribution and team emotion information in real-time. Our proof of concept provides a real-time analysis of teamwork discussions and visualises team member emotions, the roles they have adopted and overall team sentiment during the course of a collaborative problem-solving project. We demonstrate and discuss how such a tool could be useful for SE team management and training and the development of teamwork skills in SE university courses.

This paper investigates the effect of different forms of concept maps as scaffolding techniques to support answering quizzes in an online learning environment. Concept maps which represent a course topic have being utilised as a scaffolding technique for learning the subject matters and problem solving. However, due to the typical amount of information presented in the topic concept maps, learners might feel overwhelmed, reducing their motivation and increasing the learners' disorientation. In order to overcome this issue, a study was conducted with 59 undergraduates of a Software Engineering course to measure the effect of different forms of concept maps on learning. The study obtained statistically significant results when using concept maps adapted to given quizzes (known as task-adapted concept maps). Students' reflections collected through a questionnaire were very positive towards task-adapted concept maps as a scaffolding technique.

There is widespread concern about lack of student engagement in Information and Communication Technology (ICT) courses and the influence of this on learning outcomes, retention, and the student experience overall. Lack of engagement is particularly concerning for first-year students, who are developing their study behaviours for the remainder of their degree programs. This study seeks to report on the myriad of current initiatives in Australian universities to increase ICT student engagement. This is explored for both in-class teaching innovations and the support structures with which students interface academically and administratively. The study draws upon data collected from interviews of 30 academics involved with the design and delivery of the first-year learning experience of ICT students in 25 Australian universities. Analysis of this data has provided a comprehensive overview of current initiatives to address student engagement. These covered a range of academic and non-academic aspects of the student experience. Our findings highlight the unique challenges that our first-year ICT students face and we recommend areas for further investigation.

Despite increased attention from Universities and Industry, the low representation of female students in Computer Science undergraduate degrees remains a major issue. Recognising this issue, leading tech companies have established strong and committed diversity initiatives but have only reached up to 17\% female representation in their tech departments. The causes of the reduced attraction and retention of female students are varied and have been widely studied, advancing the understanding of why female students do not take up or leave Computer Science. However, few analyses look at the perceptions of the females that have stayed in the field. In this paper, we explore the viewpoints of female academics and postgraduate students in Computer Science with various undergraduate backgrounds and pathways into academia. Our analysis of their interviews shows the influence of family, exposure, culture, sexism and gendered thought on their perceptions of the field, and of themselves and their peers. We identify that perceptions of identity conflict and a lack of belonging to the discipline persist even for these high-performing professionals.

Industry has called upon academia to better prepare Computer Science graduates for teamwork, especially in developing the soft skills necessary for collaborative work. However, the teaching and assessment of teamwork is not easy, with instructors being pressed for time and a lack of tools available to efficiently analyse student teamwork, where large cohorts are involved. We have developed a teamwork dashboard, founded on learning analytics, learning theory and teamwork models that analyses students' online teamwork discussion data and visualises the team mood, role distribution and emotional climate. This tool allows educators to easily monitor teams in real-time. Educators may use the tool to provide students with feedback about team interactions as well as to identify problematic teams. We present a case study, trialing the dashboard on one university Computer Science course and include reflections from the course lecturer to determine its utility in monitoring online student teamwork.

The diagnosis, prediction, and understanding of unexpected behavior is crucial for long running, large scale distributed systems. However, existing works focus on the identification of faults in specific time moments preceded by significantly abnormal metric readings, or require a previous analysis of historical failure data. In this work, we propose an online behavior classification system to identify a wide range of undesired behaviors, which may appear even in healthy systems, and their evolution over time. We employ a two-step process involving two online classifiers on periodically collected system metrics to identify at runtime normal and anomalous behaviors such as deadlock, starvation and livelock, without any previous analysis of historical failure data. Our approach achieves over 80% accuracy in detecting unexpected behaviors and over 90% accuracy in identifying their type with a short delay after the anomalies appear, and with minimal expert intervention. Our experimental analysis uses system execution traces obtained from a Google cluster and from our in-house distributed system with varied behaviors, and shows the benefits of our approach as well as future research challenges.

Concept maps are commonly used tools for organising and representing knowledge in order to assist meaningful learning. Although the process of constructing concept maps improves learners’ cognitive structures, novice students typically need substantial assistance from experts. Alternatively, expert-constructed maps may be given to students, which increase the workload of academics. To overcome this issue, automated concept map extraction has been introduced. One of the key limitations is the lack of an evaluation framework to measure the quality of machine-extracted concept maps. At present, researchers in this area utilise human experts’ judgement or expert-constructed maps as the gold standard to measure the relevancy of extracted knowledge components. However, in the educational context, particularly in course materials, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information that has to be organised. Therefore, this paper introduces a machine-based approach which studies the relative importance of knowledge components and organises them hierarchically. We compare machine-extracted maps with human judgment, based on expert knowledge and perception. This paper describes three ranking models to organise domain concepts. The results show that the auto-generated map positively correlates with human judgment (rs~1) for well-structured courses with rich grammar (well-fitted contents).

Computer Science students struggle to develop fundamental programming skills and software development processes. Crucial to successful mastery is the development of discipline specific cognitive and metacognitive skills, including self-regulation. We can assist our students in the process of reflection and self-regulation by identifying and articulating successful self-regulated learning strategies for specific discipline contexts. However, in order to do so, we must develop an understanding of those discipline-specific strategies that are successful and can be readily adopted by students.

Computer-based assessment is a useful tool for handling large-scale classes and is extensively used in the automated assessment of student programming assignments in Computer Science. The forms that this assessment takes, however, can vary widely from simple acknowledgement to a detailed analysis of output, structure and code. This study focusses on output analysis of submitted student assignment code and the degree to which changes in automated feedback influence student marks and persistence in submission. Data was collected over a four year period, over 22 courses but we focus on one course for this paper. Assignments were grouped by the number of different units of automated feedback that were delivered per assignment to investigate if students changed their submission behaviour or performance as the possible set of marks, that a student could achieve, changed. We discovered that pre-deadline results improved as the number of feedback units increase and that post-deadline activity was also improved as more feedback units were available.

The development of a coherent curriculum, encapsulating appropriate topics, learning materials and assessment, is crucial for a successful educational experience. However, designing such a curriculum is a complicated task, with challenges in tracing the development of concepts across multiple courses and ensuring that assessment is at an appropriate level at specific points in the curricula.

Autonomous defence systems are typically characterised by hard constraints on space, weight and power. These constraints have a strong impact on the non-functional properties, and performance, of the final system. System execution modelling tools permit early prediction of the performance of model driven systems, however the focus to date has been on understanding the performance of a model rather than determining if it meets performance requirements, and subsequently carrying out analysis to reveal the causes of any requirement violations. In this paper, we propose an integrated approach to performance prediction of model-driven distributed real time embedded defence systems. Our architectural prototyping system supports a scenario-driven experimental platform for evaluating model suitability within a set of deployment and real-time performance constraints. We present an overview of our performance prediction system, demonstrating the integration of modelling, execution and visualisation, and discuss a case study to illustrate our approach.

This session will help participants understand the importance of, and challenges in, introducing collaborative learning within introductory Computer Science curricula. At the University of Adelaide, we have designed our first year curriculum, a sequence of three courses, around collaborative learning - in this session we explore our experiences in developing collaborative activities, collaborative assessment and appropriate training for staff, academic and sessional, involved in collaborative sessions. Based on a seven year programme of designing and undertaking collaborative activities, we will discuss our successes, and our failures, in the use of collaborative learning techniques.

Collaborative learning encourages deeper learning, producing significant benefit in learning outcomes. There has been an increasing trend to adopt collaborative activities, due to the expected learning benefits but also because of the expected social benefits and their impact on transition concerns. However, collaborative activities may also introduce additional stress and anxiety for students as they cope with altered participation expectations, and the need to develop collaboration, communication and management skills concurrently with their discipline skills. In this paper we describe a phenomenographic analysis of student's reflections on collaborative activities, including their perceptions of the purpose of such activities, and corresponding behaviours.

How do we identify students who are at risk of failing our courses? Waiting to accumulate sufficient assessed work incurs a substantial lag in identifying students who need assistance. We want to provide students with support and guidance as soon as possible to reduce the risk of failure or disengagement. In small classes we can monitor students more directly and mark graded assessments to provide feedback in a relatively short time but large class sizes, where it is most easy for students to disappear and ultimately drop out, pose a much greater challenge. We need reliable and scalable mechanisms for identifying at-risk students as quickly as possible, before they disengage, drop out or fail. The volumes of student information retained in data warehouse and business intelligence systems are often not available to lecturing staff, who can only observe the course-level marks for previous study and participation behaviour in the current course, based on attendance and assignment submission.

Computer Science majors must be able to communicate effectively. Industry surveys identify the development of communication and critical thinking skills as key to the reform of the higher education sector. However, academics are challenged by time and discipline content pressures, as well as a lack of familiarity with the teaching and assessment of communication skills content. There is considerable existing work in the area of communication skills development, positioned both in terms of curriculum guidelines for effective communication skills development, and example communication skills activities. However, this research is deficient in detailed, contextualised methodologies and frameworks for the development of communication skills within the Computer Science curriculum. We present a new methodology, building upon well established theoretical frameworks, designed to assist academics in the development of communication skills activities integrated with discipline content across the curriculum. We illustrate this methodology in the design of a CS1/CS2 communication skills course.

The ability to solve problems is the key to developing software, and it is an ability that can be difficult to learn. Introductory Computer Science students are often taught syntax and semantics, along with simple problems designed to develop logical, structured thinking, but authentic problem-solving skills are rarely developed at these early stages.

Video surveillance is a key technology for enhanced protection of facilities such as airports and power stations from various types of threat. Networks of thousands of IP-based cameras are now possible, but current surveillance methodologies become increasingly ineffective as the number of cameras grows. Constructing software that efficiently and reliably deals with networks of this size is a distributed information processing problem as much as it is a video interpretation challenge. This paper demonstrates a software architecture approach to the construction of large scale surveillance network software and explores the implications for instantiating surveillance algorithms at such a scale. A novel architecture for video surveillance is presented, and its efficacy demonstrated through application to an important class of surveillance algorithms.

There exists a wide spectrum of coherency models for use in distributed shared memory (DSM) systems. The choice of model for an application should ideally be based on the application's data access patterns and phase changes. However, in current systems, most, if not all of the parameters of the coherency model are fixed in the underlying DSM system. This forces the application either to structure its computations to suit the underlying model or to endure an inefficient coherency model. This paper introduces a unique approach to the provision of DSM based on the idea of compliance. Compliance allows an application to specify how the system should most effectively operate through a separation between mechanism, provided by the underlying system, and policy, pro-vided by the application. This is in direct contrast with the traditional view that an application must mold itself to the hard-wired choices that its operating platform has made. The contribution of this work is the definition and implementation of an architecture for compliant distributed coherency management. The efficacy of this architecture is illustrated through a worked example.

Little work has been done in garbage algorithms for distributed shared memory systems. Mosaic is a safe and complete garbage collection system that collects garbage in object systems that are implemented above page-based distributed shared memory systems. It is non-intrusive in its impact on application performance. Mosaic partitions the virtual address space into separately managed regions, which form the basic unit of object storage. Garbage collection operates by moving objects between these partitions in a manner that associates live objects together, leaving unreachable objects behind so that partitions may be reused. To provide for safe operation of the collector, a distributed updated protocol maintains sufficient local knowledge of pointer duplication and destruction to allow safe determination of object reachability without the need to globally trace the object space. Mosaic exploits the semantics of pointer operations in type-safe object languages to allow for a weakened consistency model of pointer update during garbage collection.