| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 839 | 13 | 12 | 5 |
E-Informatica Software Engineering Journal investigates areas of study like Software engineering, Programming language, Software, Software development and Systems engineering. Domain (software engineering) studies in the realm of Software engineering interact with fields like Systematic review. Class diagram, Unified Modeling Language, Domain model, Executable and Specification language studies are all carried out as a component of the study in Programming language presented.
The majority of Unified Modeling Language studies are focused on the issues of Sequence diagram. Concepts in Process management, as well as related topics in Capability Maturity Model Integration, are covered in the Software research presented in the journal. Software development, which encompasses Software construction, Software walkthrough, Software analytics, Software peer review and Social software engineering, is the main subject of the journal.
The journal explores research in Systems engineering alongside concepts in Agile software development and other areas of study in Agile usability engineering. Research in Artifact-centric business process model and the interrelating topic of Business Process Model and Notation were among the subjects of interest in the Business Process Execution Language studies discussed in it. The in-depth study on Engineering management also explores topics in the intersecting field of Project management.
The most cited publications are mainly concerned with subjects like Process management, Reliability engineering, Software engineering, Agile software development and Data mining. In addition to Software engineering research, the journal papers aim to explore topics under Real-time computing and Requirements analysis. In addition to Agile software development research, the published papers aim to explore topics under Standard CMMI Appraisal Method for Process Improvement, Process area, Agile usability engineering, Systems engineering and Agile Unified Process.
The discussions in e-Informatica Software Engineering Journal mainly cover the fields of Artificial intelligence, Machine learning, Control (management), Swarm intelligence and Regression testing. The journal features Artificial intelligence research that overlaps with concepts in Text mining. While it focused on Machine learning, it was also able to explore topics like Non-functional requirement and Requirements engineering.
E-Informatica Software Engineering Journal focuses on Control (management) but the discussions also offer insight into other areas such as Software and Software engineering. E-Informatica Software Engineering Journal explores the study of Swarm intelligence to improve our understanding of the broader topic of Particle swarm optimization. It facilitates the exploration of Regression testing in relation to the other disciplines, such as Model transformation and Econometrics.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in e-Informatica Software Engineering Journal (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in e-Informatica Software Engineering Journal (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 100.00% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, nan% were posted by at least one author from the top 10 institutions publishing in the journal. Another nan% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included nan% of all publications and nan% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
It can be interesting for researchers, especially within the realm of Software Engineering, to investigate alternative career paths related to their field. For instance, teaching could be a fulfilling and rewarding option. Serving as an educator helps share the wealth of knowledge and experience, shaping the future minds within the discipline. There are many avenues a professional in this field can choose. One fascinating opportunity is imparting education as a history teacher, utilizing the skills of research, analysis, and systematic approach innate to their primary discipline of software engineering. The application of a systematic and progressive mindset, typical of a software engineer, to historical events can provide a fresh and unique perspective for students. This exchange of knowledge and ideas can be enriching for both the educator and students. If you are inclined towards a teaching career, specifically being a history teacher in Massachusetts, we suggest you to read this informative guide on how to be a history teacher in Massachusetts. The article provides a detailed roadmap on how to transition from a career in research and technology to a teaching position, specifically focusing on the intricacies of becoming a history teacher in Massachusetts. Embarking on a teaching career can provide a refreshing change of pace while still enabling the professional to stay in touch with their analytical and problem-solving skills, thereby adding depth and diversity to one's career.
David Budgen;Pearl Brereton;Nikki Williams;Sarah Drummond
(2020)Eriks Klotins;Michael Unterkalmsteiner;Panagiota Chatzipetrou;Tony Gorschek
(2021)Rajni Jindal;Ruchika Malhotra;Abha Jain;Ankita Bansal
(2021)Mohsin Irshad;Kai Petersen
(2021)For students interested in studying Computer Science in the USA, exploring related online degrees and career pathways can open up diverse opportunities. Many opt for an accelerated computer science degree online to fast-track their education while balancing work or personal commitments. These programs are designed to provide core technical skills efficiently, making them ideal for career changers or ambitious learners.
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For those contemplating broader scientific applications, the question of what can I do with an environmental science degree is important. Combining computer science with environmental science knowledge opens doors to careers in ecological modeling, climate data analysis, and sustainable technology development.
