Socialification: Social Software Elements Analysis and Design

The goal of this paper is to initiate a ‎conversation on the undergraduate teaching of ‎social software analysis and design in applications ‎which are non-social-media specific. This course ‎covers the topics required to strategically ‎‎“socialify” organizational applications to engage ‎users in the most productive way for the ‎organization. To capture this effort, we suggest the ‎term “socialification” which means the use of social ‎software design features in non-social-media ‎applications. We provide some background and ‎course goals and learning objectives as well as a ‎course structure. We then discuss issues to consider ‎when implementing a course in social software ‎elements development. We also cover the theoretical ‎grounding related to the interdisciplinary process ‎and explain how it contributes to the design of the ‎course.

Automatic Identification of Assumptions from the Hibernate Developer Mailing List

During the software development life cycle, assumptions are an important type of software development knowledge that can be extracted from textual artifacts. Analyzing assumptions can help to, for example, comprehend software design and further facilitate software maintenance. Manual identification of assumptions by stakeholders is rather time-consuming, especially when analyzing a large dataset of textual artifacts. To address this problem, one promising way is to use automatic techniques for assumption identification. In this study, we conducted an experiment to evaluate the performance of existing machine learning classification algorithms for automatic assumption identification, through a dataset extracted from the Hibernate developer mailing list. The dataset is composed of 400 'Assumption' sentences and 400 'Non-Assumption' sentences. Seven classifiers using different machine learning algorithms were selected and evaluated. The experiment results show that the SVM algorithm achieved the best performance (with a precision of 0.829, a recall of 0.812, and an F1-score of 0.819). Additionally, according to the ROC curves and related AUC values, the SVM-based classifier comparatively performed better than other classifiers for the binary classification of assumptions.</p

Architectural assumptions and their management in industry – An exploratory study

As an important type of architectural knowledge, architectural assumptions should be well managed in projects. However, little empirical research has been conducted regarding architectural assumptions and their management in software development. In this paper, we conducted an exploratory case study with twenty-four architects to analyze architectural assumptions and their management in industry. In this study, we confirmed certain findings from our previous survey on architectural assumptions (e.g., neither the term nor the concept of architectural assumption is commonly used in industry, and stakeholders may have different understandings of the architectural assumption concept). We also got five new findings: (1) architects frequently make architectural assumptions in their work; (2) the architectural assumption concept is subjective; (3) architectural assumptions are context-dependent and have a dynamic nature (e.g., turning out to be invalid or vice versa during their lifecycle); (4) there is a connection between architectural assumptions and certain types of software artifacts (e.g., requirements and design decisions); (5) twelve architectural assumptions management activities and four benefits of managing architectural assumptions were identified