An increasingly important requirement of modern software-based systems is continuous operation even in the face of environmental changes, shifting user requirements, and unanticipated faults. One approach to address t...
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ISBN:
(纸本)9780769538440
An increasingly important requirement of modern software-based systems is continuous operation even in the face of environmental changes, shifting user requirements, and unanticipated faults. One approach to address this requirement is to make systems self-adaptive: when problems are detected the system "heals" itself. In this talk I describe the Rainbow System, which allows engineers to add self-healing capabilities to existing systems. The key ideas behind Rainbow are (a) the use of architectural models;(b) a new language for specifying self-healing strategies;and (c) the ability to customize the self-healing mechanisms to the domain.
Recent advances in ubiquitous computing, autonomic computing, and artificial intelligence have led to a desire to better support user-oriented tasks by placing more intelligence In the computing infrastructure. This i...
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The proposal based studio is a method to teach software engineers best practices without developing a "cookie cutter" environment. It teaches students how to get projects done in the fixed time of a degree p...
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ISBN:
(纸本)9780769531441
The proposal based studio is a method to teach software engineers best practices without developing a "cookie cutter" environment. It teaches students how to get projects done in the fixed time of a degree program without relying on a dogmatic, fixed "recipe" process. This paper discusses how students are provided a template with six general process areas for which they must "propose" solutions. These proposed solutions are unique to each 4 to 5 member Master of softwareengineering team and are continuously reviewed by the faculty throughout the project.
In 1989 the professional masters of softwareengineering (MSE) program at Carnegie Mellon initiated a new approach to graduate level software projects: the software development studio. In 2002 the studio concept was f...
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ISBN:
(纸本)9780769531441
In 1989 the professional masters of softwareengineering (MSE) program at Carnegie Mellon initiated a new approach to graduate level software projects: the software development studio. In 2002 the studio concept was first exported under a cooperative program to teach softwareengineering at an international partner university. However, CMU quickly determined that student projects, at this partner, lacked progress compared to their CMU counterparts. This paper will focus on the critical lessons that were learned from this ldquoexportrdquo that not only has helped this partnership but also others that are just starting.
Many academic programs use industrial projects within their teaching curriculum. These provide an exposure to real customers, challenging problems currently faced by industry and the opportunity to interact with worki...
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ISBN:
(纸本)9780769531441
Many academic programs use industrial projects within their teaching curriculum. These provide an exposure to real customers, challenging problems currently faced by industry and the opportunity to interact with working professionals in a situation driven by quality, schedules, and users. Selecting projects that fit todaypsilas academic environment, is a constant challenge. This paper presents how at Carnegie Mellon we have refined our project selection criteria to provide a better fit between our needs and those of our industrial partners. Criteria, issues to consider and pitfalls to avoid in the project selection process will be discussed.
This paper presents students' performance prediction model based on Machine Learning (ML) classifiers. The used dataset provides three groups of features that may affect students' performance. Features selecti...
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This paper presents students’ performance prediction model based on Machine Learning (ML) classifiers. The used dataset provides three groups of features that may affect students’ performance. Features selection app...
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ISBN:
(数字)9798331540012
ISBN:
(纸本)9798331540029
This paper presents students’ performance prediction model based on Machine Learning (ML) classifiers. The used dataset provides three groups of features that may affect students’ performance. Features selection approaches have been applied to the dataset. Several well-known ML classifiers including Neural Network (ANN), Bayes Network (Bayes Net), and Decision Tree (DT). Along with the original approach, hyper approach is applied using boosting, bagging, and voting classifiers. The performance of these algorithms is examined and compared using a confusion matrix. Results show that behavioral features have a major impact on students’ performance and ML algorithms can be used effectively to build students’ performance prediction model.
Java has reinvigorated the debate on the `best' language to use for CS1. Much of the controversy centers on the goals of CS1, specific language constructs that either hinder or support the first formal introductio...
Java has reinvigorated the debate on the `best' language to use for CS1. Much of the controversy centers on the goals of CS1, specific language constructs that either hinder or support the first formal introduction to programming, and, even, `real-world' relevance. Missing from typical discussions is the effect of the language choice in CS1 on CS2 and subsequent courses in the CS curriculum. In all such dialogues, it is important to note the characteristics of the department at hand. While many programs can afford to choose a language (such as Scheme) purely for pedagogical reasons, others (if not most), due to pressure from students, industry, advisory boards, select a language with some market appeal. Small departments that serve students who expect an immediate transition to a professional job typically choose a traditional procedural language like C, Pascal, Modula-2, or a popular OO language like C++, Java, Ada95, or Visual Basic. Hence, we focus here on the choice of one of these languages, and the resulting effects on students' progression in the CS curriculum.
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