Call for Papers

This journal also seeks proposals for special issues. Before submitting a special issue proposal, please check the following list to ensure there are no past special issues on the same topic.

In progress Special Issues:

  • Deep Learning in Open-Source Software Ecosystems
  • Software Engineering Automation: A Natural Language Perspective
  • Deep Learning in Open-Source Software Ecosystems
  • Intelligent Software-Driven Autonomous Systems
  • Software Engineering for Parallel Programming
  • Software Defined Networks (SDN) for Environmental Surveillance  

Past Special Issues:

Volume 27

December 2020, issue 3-4

Special Issue on Automated Software Engineering for Mobile Applications

Volume 26

December 2019, issue 4

Special Section on Selected Research in Automated Software Engineering

September 2019, issue 3

Special Section on Artificial Intelligence for Requirements Engineering

Volume 25

December 2018, issue 4

Special Issue: Advanced Topics in Automated Software Engineering

March 2018, issue 1

Special section on Selected Areas in Automated Software Engineering

Volume 24

December 2017, issue 4

Special Section on Realizing Artificial Intelligence and Software Engineering Synergies and Special Section on Emerging Areas in Automated Software Engineering

September 2017, issue 3

Special Section on Search Based Software Engineering and Data Mining

March 2017, issue 1

Special Section on Automation in Software Performance Engineering

Volume 23

December 2016, issue 4

Special Section on long term evolution of software systems

March 2016, issue 1

Special Issue: Selected Topics in Automated Software Engineering (Part 3)

Volume 22

December 2015, issue 4

Special Issue: Selected Topics in Automated Software Engineering (Part 2)

September 2015, issue 3

Special Issue: Selected Topics in Automated Software Engineering (Part 1)

June 2015, issue 2

Special Issue on Realizing Artificial Intelligence and Software Engineering Synergies (Part 2)

March 2015, issue 1

Special Issue on Realizing Artificial Intelligence and Software Engineering Synergies (Part 1)

Volume 21

December 2014, issue 4

Special Issue: Automated Techniques for Migrating to the Cloud (Part 2); Guest Editors: Ching-Hsien Hsu and John Grundy

September 2014, issue 3

Special Issue: Automated Techniques for Migrating to the Cloud (Part 1); Guest Editors: Ching-Hsien Hsu and John Grundy

Volume 20

September 2013, issue 3

Special Issue on Innovative Automated Software Engineering Tools, Part Two; Guest Editors: John Grundy and John Hosking

June 2013, issue 2

Special Issue: Innovative Automated Software Engineering Tools, Part One; Guest Editors: John Grundy and John Hosking

Volume 19

December 2012, issue 4

Special Issue: Selected Topics in Automated Software Engineering

June 2012, issue 2

Special Issue: Learning to Organize Testing; Guest Editors: Ayse Bener and Tim Menzies

Volume 18

December 2011, issue 3-4

Special Issue on Selected Topics in Automated Software Engineering: Specification Mining and Defect Detection; Guest Editors: Mats P.E. Heimdahl and Gabriele Taentzer

Volume 17

June 2010, issue 2

Special Issue: Selected Works from ASE 2008; Guest Editors: Andrew Ireland and Willem Visser

Volume 16

June 2009, issue 2

Special Issue: JASE on Source Code Analysis and Manipulation; Guest Editors: Michael W. Godfrey and Bogdan Korel

March 2009, issue 1

Special Issue: 22nd International Conference on Automated Software Engineering (ASE 2007); Guest Editors: Alexander Egyed and Bernd Fischer

Volume 15

December 2008, issue 3-4

Special Issue on "Trends in Automated Software Engineering"

March 2008, issue 1

Special Issue on Selected Papers from the 21st International Conference on Automated Software Engineering (ASE'2006)

Volume 14

June 2007, issue 2

Special Issue on selected papers from the 20th International Conference on Automated Software Engineering (ASE'2005)

March 2007, issue 1

Special Issue on selected papers from the 19th international Automated Software Engineering Conference (ASE-2004). Guest Editors: Kurt Stirewalt and Virginie Wiels

Volume 13

July 2006, issue 3

Special issue on selected papers from ASE-2003

April 2006, issue 2

Special Issue Section on Software Architecture Recovery

Volume 11

October 2004, issue 4

Selected Papers from 16th International Conference on Automated Software Engineering

June 2004, issue 3

Introduction to Special Issue on Distributed and Mobile Software Engineering

April 2004, issue 2

Special Section on Automated Verification of Infinite-State Systems

January 2004, issue 1

Special Issue on Source Code Analysis and Manipulation

That said, the journal gratefully accepts non-open science papers. Specifically, all submissions will undergo the same review process independent of their use of open science practices. Also, for industrial papers, it is understood that industrial papers have the right not to disclose their data, e.g., for confidentiality reasons.

Open science
The journal has a preference for open science practices that allows other researchers to examine and extend the published work and other people to access the results freely.   These practices include public disclosure of data, analyses scripts, artifacts, and self-archived manuscripts. Open artifacts need to be “available” as defined by the ACM artifacts standard; i.e. placed on a publicly accessible archival repository where a DOI and a link to this repository is provided (for example, a Github repo with at least one release registered at Zenodo would satisfy this criteria; see https://guides.github.com/activities/citable-code/). 

Paper Types

Technical paper; Length: 10+ pages (usually less than 50); Content: Motivation + methods 
+  new results; Archival records of completed research, usually with an evidence-based evaluation of hypotheses (e.g experiments on some SE-related data with a statistical analysis).  

Industrial paper; Length: 3+ pages (usually less than 50); Content: E.g. reports an early prototype or deployed version of an automated software engineering task, or experiences/challenges during the deployment of such automation.

Literature review; Length: 10+ pages (usually less than 50); Content: Motivation + methods 
+ summary of old results; Merely describing prior work is not enough. Literature reviews should identify gaps in prior work and propose ways to address that gap. Exceptional literature reviews actually perform some of those experiments.

Vision statement; Length: 3-8 pages; Content: Motivation; A carefully stated opinion, perhaps yet without supporting experimentation. For example, see “Edgar Dijkstra: Go To Statement Considered Harmful”,https://homepages.cwi.nl/~storm/teaching/reader/Dijkstra68.pdf

Registered report (initial); Length: 3+ pages (usually less than 50); Motivation + method; Documenting formative research ideas and a hypothesis, perhaps without a longer investigation - these should include motivation and a proposed analysis method. Ideally, registered reports are followed up by a registered results paper.

Registered results (follow-up); Length: 10+ pages (usually less than 50); Short summary of (Motivation + method) then new results; This is the second part of a “registered report” paper.  Registered reports can be explorative (of new ground) or confirmative (i.e. checking a prior result). For exploratory studies, updating the hypothesis after seeing new results is allowed, providing that update is made is a principled manner (using some principle process).

Tutorial; Length: Method (not new results); Tutorials (a) need to be based on “available” material (as defined in the “Open Science” section, above).  Such papers need to (b) review the research area addressed by some tool/ algorithm/ technique/data set and (c) suggest a list of significant open issues that could be addressed with the tool or data set. Tutorials are evaluated according to their potential to enable future work.

Tool report; Length: 3-8 pages; Method (not new results); Short report offering some notes on some reusable tool/artifact/data set describing, along with open issues with that tool or data set that could be explored further. Tool reports are evaluated according to their potential to enable future work.

 

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