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International Symposium on Software Testing and Analysis

International Symposium on Software Testing and Analysis (ISSTA)

Name: International Symposium on Software Testing and Analysis
Start: 2023-07-17
End: 2023-07-21
Location: Seattle

Location: Seattle , United States

Conference dates: 7/17/2023 - 7/21/2023

Research H-index

Official website

Ranking & Metrics

Discipline name	Position	Best Scientists	Publications	D-Index
Computer Science	56	115	211	31

Call for Papers

ISSTA invites three kinds of submissions. The clear majority of submissions is expected to be “Research Papers”, but submissions that best fit the description of “Experience Papers” or “Replicability Studies” should be submitted as such.

Research Papers
Authors are invited to submit research papers describing original contributions in testing or analysis of computer software. Papers describing original theoretical or empirical research, new techniques, methods for emerging systems, in-depth case studies, infrastructures of testing and analysis, or tools are welcome.

Experience Papers
Authors are invited to submit experience papers describing a significant experience in applying software testing and analysis methods or tools and should carefully identify and discuss important lessons learned so that other researchers and/or practitioners can benefit from the experience. Of special interest are experience papers that report on industrial applications of software testing and analysis methods or tools.

Replicability Studies
ISSTA would like to encourage researchers to replicate results from previous papers. A replicability study must go beyond simply re-implementing an algorithm and/or re-running the artifacts provided by the original paper. It should at the very least apply the approach to new, significantly broadened inputs. Particularly, replicability studies are encouraged to target techniques that previously were evaluated only on proprietary subject programs or inputs. A replicability study should clearly report on results that the authors were able to replicate as well as on aspects of the work that were not replicable. In the latter case, authors are encouraged to make an effort to communicate or collaborate with the original paper’s authors to determine the cause for any observed discrepancies and, if possible, address them (e.g., through minor implementation changes). We explicitly encourage authors to not focus on a single paper/artifact only, but instead to perform a comparative experiment of multiple related approaches.

In particular, replicability studies should follow the ACM guidelines on replicability (different team, different experimental setup): The measurement can be obtained with stated precision by a different team, a different measuring system, in a different location on multiple trials. For computational experiments, this means that an independent group can obtain the same result using artifacts which they develop completely independently. This means that it is also insufficient to focus on reproducibility (i.e., different team, same experimental setup) alone. Replicability Studies will be evaluated according to the following standards:

Depth and breadth of experiments
Clarity of writing
Appropriateness of conclusions
Amount of useful, actionable insights
Availability of artifacts
We expect replicability studies to clearly point out the artifacts the study is built on, and to submit those artifacts to artifact evaluation (see below). Artifacts evaluated positively will be eligible to obtain the prestigious Results Reproduced badge.

Overview

The ranking presented on this page features a comprehensive assessment of scientific conferences within the field of Computer Science. This authoritative ranking has been meticulously prepared by Research.com, recognized as one of the leading websites for science research across all major disciplines, including Computer Science, and providing trusted data on scientific contributions since 2014.

Each conference's position in the ranking is determined based on a unique bibliometric score developed by Research.com. This score is calculated through a robust methodology that incorporates both the estimated h-index and the number of leading scientists who have participated in the conference over the past three years. Such a multidimensional approach ensures a balanced and insightful evaluation, accurately reflecting both the quality and influence of a conference within the academic community.

The Impact Score values for this ranking were diligently gathered as of 2024-11-27, ensuring the use of the most recent and relevant data available. The detailed process involved a thorough review and ranking of more than 2,742 conferences, each selected after an exhaustive analysis. This included a rigorous examination of over 148,739 scientific documents published in the last three years by a cohort of 13,184 highly esteemed and influential scientists within the Computer Science domain.

The extensive scope and depth of this analysis underscore the credibility and reliability of the ranking. For those interested in exploring the technical details and procedures employed in computing the ranking scores, a comprehensive explanation is available on our Methodology Page.

Papers citation over time

A key indicator for each conference 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.

Top authors and change over time

The top authors publishing at International Symposium on Software Testing and Analysis (based on the number of publications) are:

Alessandro Orso (17 papers) published 2 papers at the last edition,
Michael D. Ernst (17 papers) published 3 papers at the last edition,
Darko Marinov (16 papers) published 1 paper at the last edition the same number as at the previous edition,
Andreas Zeller (16 papers) absent at the last edition,
Tao Xie (13 papers) absent at the last edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing at International Symposium on Software Testing and Analysis (based on the number of publications) are:

University of Illinois at Urbana–Champaign (27 papers) published 1 paper at the last edition, 2 less than at the previous edition,
IBM (25 papers) absent at the last edition,
Saarland University (24 papers) published 1 paper at the last edition, 3 less than at the previous edition,
Microsoft (23 papers) absent at the last edition,
Georgia Institute of Technology (22 papers) published 2 papers at the last edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

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 2018 edition, 2.50% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 30.77% were posted by at least one author from the top 10 institutions publishing at the conference. Another 12.82% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 15.38% of all publications and 41.03% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference 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 conference in relation to all participants in a given year.

Returning Institution Index

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.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, 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:

Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

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