RadGrad is a curriculum initiative implemented via a web-based application that combines features of social networks, degree planners, and serious games. RadGrad redefines the traditional meaning of "progress" and "success" in the undergraduate computer science degree program, with the ultimate goal of improving student engagement, diversity, and retention. In this paper, we relate RadGrad to other curriculum initiatives, overview its key functionality, present results from an evaluation conducted during its first year of deployment, and discuss our lessons learned and future directions.

We present our findings after two years of experience involving three instructors using an "athletic" approach to software engineering education (AthSE). Co-author Johnson developed AthSE in 2013 to address issues he experienced teaching graduate and undergraduate software engineering. Co-authors Port and Hill subsequently adapted the original approach to their own software courses. AthSE is a pedagogy in which the course is organized into a series of skills to be mastered. For each skill, students are given practice "Workouts" along with videos showing the instructor performing the Workout both correctly and quickly. Unlike traditional home-work assignments, students are advised to repeat the Workout not only until they can complete it correctly, but also as quickly as the instructor. In this experience report we investigate the following question: how can software engineering education be redesigned as an athletic endeavor, and will this provide more efficient and effective learning among students and more rapidly lead them to greater competency and confidence?

For five years, the Hackystat project has incremen tally developed and evaluated a generic framework for in process software engineering measurement and analysis (ISEMA). At least five other independent ISEMA system development projects have been initiated during this time, indicating growing interest and investment in this approach by the software engineering community. This pa per presents 12 important requirement and design trade offs made in the Hackystat system, some of their implications for organizations wishing to introduce ISEMA, and six directions for future research and development. The three goals of this paper are to: (1) help potential users of ISEMA systems to better evaluate the relative strengths and weaknesses of current and future systems, (2) help potential developers of ISEMA systems to better understand some of the important requirement and design tradeoffs that they must make, and (3) help accelerate progress in ISEMA by identifying promising directions for future research and development.

The challenges in fault prediction today are to get a prediction as early as possible, at as low a cost as possible, needing as little data as possible and preferably in such a language that your average developer can understand where it came from. This paper presents a fault sampling method where a summary of a few, easily available metrics is used together with the results of a few sampled classes to generalize the fault content to an entire system. The method is tested on a large software system written in Java, that currently consists of around 2000 classes and 300,000 lines of code. The evaluation shows that the fault generalization method is good at predicting fault-prone clusters and that it is possible to generalize the values of a few representative classes.

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. source control systems store changes to the source code as development progresses, . defect tracking systems follow the resolution of software defects, and . archived communications between project personnel record rationale for decisions throughout the life of a project. The data in these repositories is available for most large software projects and represents a detailed and rich record of the historical development of software systems. Until recently these repositories were used primarily for their intended activities such as maintaining versions of the source code or tracking the status of a defect. Software practitioners and researchers are beginning to recognize the potential benefit of mining this information for other purposes. Research is now proceeding to uncover the ways in which mining these repositories can help support the maintenance of software systems, improve software design/reuse, and empirically validate novel ideas and techniques. Unfortunately, software repositories are not designed to facilitate empirical understanding of a software project. Software tools (such as version control and defect tracking systems) tend to have various anomalies and issues in their recorded information. Tools may be used differently in different projects, and different tools are often used in different organizations. More importantly, the quantities of interest such as development effort are usually not directly captured. A key challenge is to build useful theories and models of software development that can be empirically validated using the information in software repositories. There has been progress in overcoming some of these challenges by constructing tools and developing methods to extract, clean, and validate information from software repositories. A workshop on the topic of mining software repositories (MSR 2004: http://msr.uwaterloo.ca) was held on 25 May 2004 in Edinburgh, UK, in conjunction with the 26th IEEE International Conference on Software Engineering (ICSE). A following workshop (MSR 2005) was held on 17 May 2005 in Saint Louis, Missouri, in conjunction with the 27th ICSE. Each workshop had submissions from over 14 countries. Both workshops had over 50 attendees, making the MSR workshop series the most attended ICSE workshop for the last two years. These workshops have established a community of researchers and practitioners who are working to recover and use the data stored in software repositories to further understanding of software development practices.

Pedagogues such as the Personal Software Process (PSP) shift metrics definition, collection, and analysis from the organizational level to the individual level. While case study research indicates that the PSP can provide software engineering students with empirical support for improving estimation and quality assurance, there is little evidence that many students continue to use the PSP when no longer required to do so. Our research suggests that this "PSP adoption problem" may be due to two problems: the high overhead of PSP-style metrics collection and analysis, and the requirement that PSP users "context switch" between product development and process recording. This paper overviews our initial PSP experiences, our first attempt to solve the PSP adoption problem with the LEAP system, and our current approach called Hackystat. This approach fully automates both data collection and analysis, which eliminates overhead and context switching. However, Hackystat changes the kind of metrics data that is collected, and introduces new privacy-related adoption issues of its own.

Article Leap: a "personal information environment" for software engineers Share on Author: Philip M. Johnson Collaborative Software Development Laboratory, Department of Information and Computer Sciences, University of Hawaii, Honolulu, HI Collaborative Software Development Laboratory, Department of Information and Computer Sciences, University of Hawaii, Honolulu, HIView Profile Authors Info & Claims ICSE '99: Proceedings of the 21st international conference on Software engineeringMay 1999 Pages 654–657https://doi.org/10.1145/302405.302919Online:16 May 1999Publication History 1citation258DownloadsMetricsTotal Citations1Total Downloads258Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The Personal Software Process (PSP) is used by software engineers to gather and analyze data about their work. Published studies typically use data collected using the PSP to draw quantitative conclusions about its impact upon programmer behavior and product quality. However, our experience using PSP in both industrial and academic settings revealed problems both in collection of data and its later analysis. We hypothesized that these two kinds of data quality problems could make a significant impact upon the value of PSP measures. To test this hypothesis, we built a tool to automate the PSP and then examined 89 projects completed by ten subjects using the PSP manually in an educational setting. We discovered 1539 primary errors and categorized them by type, subtype, severity, and age. To examine the collection problem we looked at the 90 errors that represented impossible combinations of data and at other less concrete anomalies in Time Recording Logs and Defect Recording Logs. To examine the analysis problem we developed a rule set, corrected the errors as far as possible, and compared the original and corrected data. This resulted in significant differences for measures such as yield and the cost-performance ratio, confirming our hypothesis. Our results raise questions about the accuracy of manually collected and analyzed PSP data, indicate that integrated tool support may be required for high quality PSP data analysis, and suggest that external measures should be used when attempting to evaluate the impact of the PSP upon programmer behavior and product quality.

Software review is a fundamental component of the software quality assurance process, yet significant controversies exist concerning efficiency and effectiveness of various review methods.A central question surrounds the use of meetings: traditional review practice views them as essential, while more recent findings question their utility.We conducted a controlled experimental study to assess several measures of cost and effectiveness for a meeting and non-meeting-based review method.The experiment used CSRS, a computer mediated collaborative software review environment, and 24 three person groups.Some of the data we collected included: the numbers of defects discovered, the effort required, the presence of synergy in the meeting-based groups, the occurrence of false positives in the non-meetingbased groups, and qualitative questionnaire responses.This paper presents the motivation for this experiment, its design and implementation, our empirical findings, conclusions, and future directions.

Software review is a fundamental tool for software quality assurance. Nevertheless, there are significant controversies as to the most efficient and effective review method. One of the most important questions currently being debated is the utility of meetings. Although almost all industrial review methods are centered around the inspection meeting, recent findings call their value into question. In prior research the authors separately and independently conducted controlled experimental studies to explore this issue. The paper presents new research to understand the broader implications of these two studies. To do this, they designed and carried out a process of "reconciliation" in which they established a common framework for the comparison of the two experimental studies, reanalyzed the experimental data with respect to this common framework, and compared the results. Through this process they found many striking similarities between the results of the two studies, strengthening their individual conclusions. It also revealed interesting differences between the two experiments, suggesting important avenues for future research.

Current collaborative learning systems focus on maximizing shared information. However, “meaningful learning” is not simply information sharing but, more importantly, knowledge construction. CLARE is a computer-supported learning environment that facilitates meaningful learning through collaborative knowledge construction. CLARE provides a semi-formal representation language called RESRA and an explicit process model called SECAI. Experimental evaluation through 300 hours of classroom usage indicates that CLARE does support meaningful learning, and that a major bottleneck to computer-mediated knowledge construction is summarization. Lessons learned through the design and evaluation of CLARE provide new insights into both collaborative learning systems and collaborative learning theories.