Research data for Mitigating Safety Risks in Information Systems: A Self-Adaptive Approach Research Data. A controlled experiment was conducted to determine whether SAS, aware of interruptions, improves effectiveness, efficiency, situational awareness, and usability compared to non-adaptive systems in information systems. A total of 30 participants contributed to the controlled experiment.
This dataset presents a comprehensive collection of research articles on requirements engineering, encompassing various subtopics such as requirements specification, elicitation techniques, software development methodologies, and challenges in requirements management. The dataset includes metadata such as publication year, author details, research titles, objectives, problem statements, identified gaps, key findings, and contributions.
The Software Engineering Skillset Dataset focuses on three critical areas: Soft Skills, Life Skills, and Technical Skills. These components are essential for assessing and predicting the software engineering competencies of computer science students. Data for the dataset was collected through a carefully designed questionnaire, targeting students enrolled in software engineering courses at higher educational institutions across Kerala, India. Over two thousand students participated in the survey, which used a dedicated assessment quiz with fifteen questions to evaluate each skill category.
The requirements, their types and priorities are gathered from 43 project teams which will be uselful to automate the phases of requirement engineering i.e. requirements classification and prioritisation. As the publicly available datasets do not contain the complete information (type and priority) about requirements, the dataset is created by collecting the data from 43 BTech project groups. This dataset includes 11 different types of software requirements. The dependency of requirements is also considered while gathering requirements from the project teams.
Overview
The dataset under consideration is a comprehensive compilation of code snippets, function descriptions, and their respective binary representations aimed at fostering research in software engineering. It contains a variety of code functionalities and serves as a valuable resource for understanding the behavior and characteristics of C programs. This data is sourced from the AnghaBench repository, a well-documented collection of C programs available on GitHub.
Columns and Data Types
The dataset contains the following columns:
Software engineering metrics collected via SourceMeter for open-source Java repositories.
Companion data of the paper "Using social media and personality traits to assess software developers’ emotions" submitted to the IEEE Access journal, 2022. This dataset contains the anonymized dataset used in the study, including the answers of demographic survey, the answers to the Big Five Inventory, the experiment protocol, the manual analysis from psychologists and participants, all generated charts and data analysis.
Component based software development (CBSD) is a trend in the indusrty. This dataset contains the size of the components in various softwares viz. WEKA, Apache crypto library, Apache Codec library and basic linear alebra library by M. Elsheimy. The first three are in Java and last one is in C# programming languages, From the WEKA repository, 11 packages have been considered,
Experimental evaluation of test-driven development with interns working on a real industrial project:
- raw experimental data: unit test, integration tests results and coverage,
- R statistical analysis script,
- processing scripts.
A fully-labeled C++03 program dataset provides a unique resource to evaluate model checkers in respect to language coverage. To tackle modern aspects of the C++ language, a large-scale benchmark dataset includes more than 1,500 C++03-compliant programs, which cover different aspects of the language, including exception handling, templates, inheritance, polymorphism, the standard template library, and object-oriented design.
