INFORMATION AND SOFTWARE TECHNOLOGY

Journal Title
INFORMATION AND SOFTWARE TECHNOLOGY

INFORM SOFTWARE TECH

ISSN / eISSN
0950-5849
Aims and Scope
Information and Software Technology is the international archival journal focusing on research and experience that contributes to the improvement of software development practices. The journal's scope includes methods and techniques to better engineer software and manage its development. Articles submitted for review should have a clear component of software engineering or address ways to improve the engineering and management of software development. Areas covered by the journal include:

• Software management, quality and metrics,
• Software processes,
• Software architecture, modelling, specification, design and programming
• Functional and non-functional software requirements
• Software testing and verification & validation
• Empirical studies of all aspects of engineering and managing software development

Short Communications is a new section dedicated to short papers addressing new ideas, controversial opinions, "Negative" results and much more. Read the Guide for authors for more information.

The journal encourages and welcomes submissions of systematic literature studies (reviews and maps) within the scope of the journal. Information and Software Technology is the premiere outlet for systematic literature studies in software engineering.
Subject Area

COMPUTER SCIENCE, INFORMATION SYSTEMS

COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore
9.20 View Trend
CiteScore Ranking
Category Quartile Rank
Computer Science - Computer Science Applications Q1 #83/792
Computer Science - Information Systems Q1 #51/379
Computer Science - Software Q1 #60/404
Web of Science Core Collection
Science Citation Index Expanded (SCIE) Social Sciences Citation Index (SSCI)
Indexed -
Category (Journal Citation Reports 2023) Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS - SCIE Q2
COMPUTER SCIENCE, SOFTWARE ENGINEERING - SCIE Q2
H-index
88
Country/Area of Publication
NETHERLANDS
Publisher
Elsevier
Publication Frequency
Monthly
Annual Article Volume
148
Open Access
NO
Contact
ELSEVIER SCIENCE BV, PO BOX 211, AMSTERDAM, NETHERLANDS, 1000 AE
Verified Reviews
Note: Verified reviews are sourced from across review platforms and social media globally.
August 2020 submission
January 2021 minor revision
April 2022 acceptance
Quanyi Zou, et al. "Joint feature representation learning and progressive distribution matching for cross-project defect prediction." Information and Software Technology (2021).
2021-04-12

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