Computer Science Review Impact Factor

The first signal is that the journal is influential, not peripheral. A 12.7 impact factor and 38.4 CiteScore place Computer Science Review among the higher-authority review venues in computer science.

The second signal is that review journals work differently. A strong number here does not mean the journal is broad in article type. It means the journal is rewarding a narrow format very well: high-value surveys that help general computer-science readers understand a field.

The third signal is that the article form is doing real work. Because the journal is not built around routine primary research volume, strong citation numbers usually reflect papers that become orientation points for a topic, not just papers that publish incremental results.

The official journal page is the authoritative source for the current impact factor and CiteScore on this page. For the longer directional view, the table below uses the open Scopus-based impact-score series as a trend proxy.

Directionally, the open Scopus-based signal is down from 19.83 in 2023 to 18.39 in 2024, but still extremely strong compared with the earlier history of the journal. The healthier interpretation is not that the journal weakened. It is that the venue remains highly influential even after a slight normalization from a recent peak.

The mistake is to see a strong impact factor and assume any long review article should aim here.

That is usually wrong. Computer Science Review is restrictive in article type. The official guidance says the journal publishes research surveys and expository overviews of open problems and that the treatment should be more than a catalogue of known results. Expanded versions of primary research papers are generally not acceptable.

So the number can flatter the fit.

A technically strong manuscript can still be wrong here if it is:

• too narrow for a general computer-science audience
• mostly a literature inventory rather than a critical synthesis
• a disguised research paper with a large related-work section
• missing real open-problem framing

In our pre-submission review work with manuscripts targeting Computer Science Review, the repeating problem is not low technical quality. It is low survey maturity.

The common misses are:

The paper has coverage but not judgment. It lists methods, papers, or architectures accurately, but does not compare them critically enough.

The topic is too narrow. A useful review for a specialist community can still be too bounded for the broad readership the journal names explicitly.

The article is too attached to the authors' own work. That often makes the manuscript feel like a long positioning document rather than a field guide.

The open-problem framing is thin. Computer Science Review expects the paper to orient future work, not just summarize completed work.

If that sounds familiar, a survey-readiness check is usually more useful than another formatting pass.

The official journal insights page adds one especially useful signal beyond the impact factor: the journal's publishing timeline is relatively transparent.

Those numbers reinforce the article-type reality. The desk can be quick because the journal knows what it wants. The rest of the timeline reflects how much work a true expert survey often still needs.

The impact factor does not tell you whether the manuscript is broad enough, interpretive enough, or mature enough as a survey. It also does not tell you whether a specialist review venue or a monograph-style venue would be a more honest match.

Those are the real editorial screens.