Software Engineering Review (SER) aims to acknowledge the impact of software on today's research practice, and on new scientific discoveries in almost all research domains.

SER also aims to stress the importance of the software developers who are, in part, responsible for this impact.

SER aims to support publication of research software in such a way that:

  • The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact;
  • The software developers are given the credits they deserve;
  • The software is citable, allowing traditional metrics of scientific excellence to apply;
  • The academic career paths of software developers are supported rather than hindered;
  • The software is publicly available for inspection, validation, and re-use.

 

Above all, Software Engineering Review aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains.

 

The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below:

  • Mathematical and Physical Sciences;
  • Environmental Sciences;
  • Medical and Biological Sciences;
  • Humanities, Arts and Social Sciences.

 

Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. Software Engineering Review specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.

 

Specific topic areas include:

  • development and maintenance methods and models, e.g., techniques and principles for the specification, design, and implementation of software systems, including notations and process models;
  • assessment methods, e.g., software tests and validation, reliability models, test and diagnosis procedures, software redundancy and design for error control, and the measurements and evaluation of various aspects of the process and product;
  •  software project management, e.g., productivity factors, cost models, schedule and organizational issues, standards;
  • tools and environments, e.g., specific tools, integrated tool environments including the associated architectures, databases, and parallel and distributed processing issues;
  • system issues, e.g., hardware-software trade-off; and
  • state-of-the-art surveys that provide a synthesis and comprehensive review of the historical development of one particular area of interest.
 
Impact Factor
  • Several journal metrics are calculated. The first metric is an alternative impact factor which is based on Google Scholar's citation count.
  • The journal impact factor (JIF) normally referred to is the proprietary journal impact factor from Thomson Reuters calculated based on the Web of Science (WOS) and published in the Journal Citation Reports® (JCR). We call this the JCR®JIFDOAJ writes: "There is only one official, universally recognised impact factor that is generated by Thomson Reuters; it is a proprietary measure run by a profit making organisation. It runs against the ethics and principles of open access." This journal has no JCR®JIF, but an alternative Google-based impact factor.
  • Today most of readers find their way to search articles via Google Scholar. No open or proprietary database is directing so many readers to search articles. Google Scholar is the only openly available database suitable for journal metric calculation. It has a wide coverage and is a meaningful source. For this reason, ScholarChain is calculating its own Impact Factor based on Google Scholar's citation counts. Scientists are used to Thomson Reuters' way of calculating an impact factor. For this reason, ScholarChain applies Thomson Reuters'(TR) algorithm as published on http://wokinfo.com/essays/impact-factor in Figure 1. This algorithm is not protected and can be used by anyone. In short: SCIRP calculates a 2-year Google-based Journal Impact Factor (2-GJIF).
Example:
A = total cites in 2020 = 153
B = 2020 cites to articles published in 2018 - 2019 = 42 (this is a subset of A)
C = number of articles published in 2018 - 2019 = 35
2-GJIF for 2020 = D = B/C = 42/35 = 1.2 (TR algorithm, Google citations, data September 2019)
 
An impact factors for e.g. 2020 can only be published once this year is over (e.g. in 2021). At Thomson Reuters this is done when all 2020 publications have been processed. Once published, the JCR®JIF for a given year is fixed. In contrast, a GJIF has never a fixed value. Depending on individual activities on the Internet (self-archiving and Green Open Access), some articles published Closed Access in one year may appear online only months or even years later. This has an influence on Google Scholar's citation count and makes it necessary to state the 2-GJIF for a given year always with the date the data was retrieved from Google Scholar. SCIRP may provide updates of the 2-GJIF during the year.
 
E = 2020 self-citations to articles published in 2018 - 2019 = 6 (this is a subset of B)
Self-Cited Rate = E/B = 6/42 = 14.3 % (definition Rousseau 1999, data September 2019)
Journal self-citations are citations to articles in the same journal. A Self-Cited Rate below 20 % is considered acceptable. A higher Self-Cited Rate than this could be explained by a journal's novel or highly specific topic, but could also reveal a journal with excessive self-citations.
Please interpret the 2-GJIF with caution:
• Due to differences in the underlying database, the value calculated here for the 2-GJIF can not be compared with a JCR®2-JIF.
• Do not compare journals from different subject fields based on their JIF. Journals in fundamental subject fields tend to have higher impact factors than journals in specialized or applied subject fields.
• Journal metrics should not be used to assess individual authors. Please refer instead to our article metrics provided for each paper: Number of citations from Google Scholar and number of citations from CrossRef.
 
2. h-index

h-index (based on the Google Scholar Citations)
The current h-index considers citations from the start of the journal. It is a cumulative index that grows each year. 
 
3. h5-index

h5-index (based on the Google Scholar Metrics)
The listed h5 index is originated from Google Scholar Metrics. Google Scholar Metrics provide an easy way for authors to quickly gauge the visibility and influence of recent articles in scholarly publications.