| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 114 | 217 | 525 | 39 |
The journal facilitates discussions on Software, Software engineering, Empirical research, Software development and Data mining. The journal holds forums on Software that merges themes from other disciplines such as Quality (business), Context (language use) and Source code. Some problems in Source code that were presented in the journal overlapped with concepts under Information retrieval and Code (cryptography).
Topics in Software engineering were tackled in line with various other fields like Software maintenance, Systems engineering, Personal software process, Software construction and Process (engineering). Software sizing is a key component of Software construction research discussed in it. The studies on Empirical research discussed can also contribute to research in the domains of World Wide Web and Data science.
Empirical Software Engineering primarily discusses Software development topics, particularly Social software engineering and Software development process. Machine learning and Artificial intelligence are some topics wherein Data mining research discussed in Empirical Software Engineering have an impact. Studies on Artificial intelligence discussed in Empirical Software Engineering link to the field of Natural language processing.
The most cited articles generally zeroe in on subjects such as Software, Software engineering, Data mining, Empirical research and Software development. While the most cited articles focused on Software, they were also able to explore topics like Context (language use), World Wide Web and Information retrieval. The published articles deal with Software engineering in conjunction with Process (engineering) and similar fields in Component (UML) and Replication (computing).
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 Empirical Software Engineering (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 Empirical Software Engineering (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 2022 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.
An essential section that is not mentioned in the article is the Future Impact Predictions for the field of Empirical Software Engineering. Often, researchers and software professionals who follow such journals are interested in knowing the future trends in their field of interest in order to stay ahead of the curve. This section can provide some predictive analysis drawn from the discussions in the journals.
By analyzing previous research trends in Empirical Software Engineering, prominent scholars have suggested that the future will see an increased emphasis on the integration of machine learning and artificial intelligence in software development. As software becomes more complex, the need for automated testing and correction will also increase, opening the field to new ideas and research.
The rise of data science and the World Wide Web will also influence future research. The necessity for engineers to understand data mining and predictive analytics can impact how software is developed, maintained, and enhanced. This could lead to new sub-disciplines in the field.
In addition, there has been an increasing interest in the role of software in education. For example, software development solutions for education are becoming more necessary as the global education spectrum shifts to digital learning platforms. This could lead to a greater demand for software engineers specializing in the educational sector. You can learn more about career advancements like becoming an elementary school teacher utah salary.
The interdisciplinary nature of software engineering is likely to be a significant trend in the future, as it has been in the past. Software engineers will need to think outside the traditional confines of the discipline and perhaps broaden their skills.
Although these future impact predictions for the field of Empirical Software Engineering may change due to various factors, such transformative changes could pave the way for new research topics and discussions in the journal.
Anil Koyuncu;Kui Liu;Tegawendé François D Assise Bissyande;Dongsun Kim
(2020)Paul Ralph;Sebastian Baltes;Gianisa Adisaputri;Richard Torkar;Richard Torkar
(2020)Xing Hu;Ge Li;Xin Xia;David Lo
(2020)Vincenzo Riccio;Gunel Jahangirova;Andrea Stocco;Nargiz Humbatova
(2020)Gustavo Ansaldi Oliva;Ahmed E. Hassan;Zhen Ming (Jack) Jiang
(2020)Carmine Vassallo;Sebastiano Panichella;Fabio Palomba;Sebastian Proksch
(2020)Unknown
(2022)Zohreh Sharafi;Bonita Sharif;Yann-Gaël Guéhéneuc;Andrew Begel
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