Learning Effective Changes for Software Projects

The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by "planning" about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu

Rationale in Development Chat Messages: An Exploratory Study

Chat messages of development teams play an increasingly significant role in software development, having replaced emails in some cases. Chat messages contain information about discussed issues, considered alternatives and argumentation leading to the decisions made during software development. These elements, defined as rationale, are invaluable during software evolution for documenting and reusing development knowledge. Rationale is also essential for coping with changes and for effective maintenance of the software system. However, exploiting the rationale hidden in the chat messages is challenging due to the high volume of unstructured messages covering a wide range of topics. This work presents the results of an exploratory study examining the frequency of rationale in chat messages, the completeness of the available rationale and the potential of automatic techniques for rationale extraction. For this purpose, we apply content analysis and machine learning techniques on more than 8,700 chat messages from three software development projects. Our results show that chat messages are a rich source of rationale and that machine learning is a promising technique for detecting rationale and identifying different rationale elements.Comment: 11 pages, 6 figures. The 14th International Conference on Mining Software Repositories (MSR'17

Adapting cooperative learning to teach software architecture in multiple-role teams

The software architecture process depends on successful teamwork involving cooperation among members of the design team, cooperation between the design team and the clients, and cooperation between the design team and the development organization. Cooperative learning is a pedagogy that directly supports this type of teamwork. Through cooperative learning students realize their interdependence, practice face-to-face communication, recognize their individual accountability to the success of the group, practice interpersonal and small-group skills, and engage in frequent reflective processing of their achievements. We have adapted cooperative learning to teach software architecture in two undergraduate software engineering programs. In traditional cooperative learning, students work on one team for an extended period. This helps foster acceptance of individual differences and promotes successful teamwork. In our courses we kept students together on the same teams, but we wanted students to play multiple roles: clients, architects, and developers. So, we let the teams change roles during the course. That is, for each project one team played the role of architects, while other teams played the roles of clients and developers. Student teams rotated roles on different projects throughout the term. A further variation in cooperative learning is that, to succeed on each project, three different teams also had to cooperate. These innovations kept the benefits of cooperative learning while also exposing the students to 3 different perspectives as they progressed through their projects. This is especially important for software architecture, where the 3 perspectives must always be kept in mind. An additional benefit was that each student participated in 3 different projects, thus experiencing greater diversity of architectural challenges than would have otherwise been possible. Some changes to the traditional classroom setting are necessary in order to practice this new method. Students need to work in small teams, 3 or 4 students at most, during regularly scheduled classroom hours. The roles of individual teams must be scheduled so that sufficient time is available for each team to play each role. Fortunately, software architecture lends itself to short periods of intense team activity, with reporting and peer review of results later. We believe that this active learning style is an effective approach for most subjects, but especially for software architecture

Challenges (and Opportunities!) of a Remote Agile Software Engineering Project Course During COVID-19

COVID-19 and its immediate impacts on teaching activities have required changes from computer science educators worldwide. We switched our on-site courses to remote setups without detailed knowledge of what tools, techniques, and methods would work in different teaching contexts. A growing amount of experience reports on general best practices for remote teaching in higher education are available. However, university courses featuring practical software development projects present unique challenges regarding remote learning, as effective student collaboration is vital. In these courses, students tackle situations in the project and their team meetings that would also occur in real software projects experienced in industry settings. In this paper, we share our experiences on how we successfully adapted our software engineering project course to a remote setup, which challenges we observed in student teams and how they can be mitigated, and what (surprisingly) worked better than expected. Finally, we propose improvements that we expect will be beneficial not only for future remote-only but also for hybrid or on-site courses

Factors Affecting the Adoption of Faculty-Developed Academic Software: A Study of Five iCampus Projects

Instruction in higher education must adapt more rapidly to: changes in workforce needs, global issues, advances in disciplines, and resource constraints. The pace of such improvement depends on the speed with which new ideas and materials are adopted across institutions. In 1999 Microsoft pledged $25 million and staff support for iCampus, a seven-year MIT project to develop pioneering uses of educational technology. The TLT Group studied five iCampus projects in order to identify factors affecting institutionalization and widespread dissemination. Among the factors impeding adoption: lack of rewards and support for faculty to adopt innovations; faculty isolation; and a lack of attention to adoption issues among projects selected for funding. The study made recommendations for universities, foundations, government agencies and corporations: 1) continue making education more authentic, active, collaborative, and feedback-rich; 2) create demand to adopt ideas and materials from other sources by encouraging all faculty members to improve and document learning in their programs, year after year; 3) nurture coalitions for instructional improvement, across and within institutions; 4) create more effective higher education corporate alliances; and 5) improve institutional services to support faculty in educational design, software development, assessment methods, formative evaluation, and/or in sharing ideas with others who teach comparable courses

Mining Fix Patterns for FindBugs Violations

In this paper, we first collect and track a large number of fixed and unfixed violations across revisions of software. The empirical analyses reveal that there are discrepancies in the distributions of violations that are detected and those that are fixed, in terms of occurrences, spread and categories, which can provide insights into prioritizing violations. To automatically identify patterns in violations and their fixes, we propose an approach that utilizes convolutional neural networks to learn features and clustering to regroup similar instances. We then evaluate the usefulness of the identified fix patterns by applying them to unfixed violations. The results show that developers will accept and merge a majority (69/116) of fixes generated from the inferred fix patterns. It is also noteworthy that the yielded patterns are applicable to four real bugs in the Defects4J major benchmark for software testing and automated repair.Comment: Accepted for IEEE Transactions on Software Engineerin

Embedding accessibility and usability: considerations for e-learning research and development projects

This paper makes the case that if e‐learning research and development projects are to be successfully adopted in real‐world teaching and learning contexts, then they must effectively address accessibility and usability issues; and that these need to be integrated throughout the project. As such, accessibility and usability issues need to be made explicit in project documentation, along with allocation of appropriate resources and time. We argue that accessibility and usability are intrinsically inter‐linked. An integrated accessibility and usability evaluation methodology that we have developed is presented and discussed. The paper draws on a series of mini‐case studies from e‐learning projects undertaken over the past 10 years at the Open University

Test Case Selection and Prioritization Using Machine Learning: A Systematic Literature Review

Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities. With the wide adoption of Continuous Integration (CI) in software projects, which increases the frequency of running software builds, running all tests can be time-consuming and resource-intensive. To alleviate that problem, Test case Selection and Prioritization (TSP) techniques have been proposed to improve regression testing by selecting and prioritizing test cases in order to provide early feedback to developers. In recent years, researchers have relied on Machine Learning (ML) techniques to achieve effective TSP (ML-based TSP). Such techniques help combine information about test cases, from partial and imperfect sources, into accurate prediction models. This work conducts a systematic literature review focused on ML-based TSP techniques, aiming to perform an in-depth analysis of the state of the art, thus gaining insights regarding future avenues of research. To that end, we analyze 29 primary studies published from 2006 to 2020, which have been identified through a systematic and documented process. This paper addresses five research questions addressing variations in ML-based TSP techniques and feature sets for training and testing ML models, alternative metrics used for evaluating the techniques, the performance of techniques, and the reproducibility of the published studies