2022 Proceeding- General Chair:
- Shane McIntosh,
- Program Chairs:
- Weiyi Shang,
- Gema Rodriguez Perez
It is our pleasure to welcome you to the 18th ACM International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2022), to be held in hybrid mode (physically and virtually) on November 18th, 2022, co-located with the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022). PROMISE is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in the construction and/or application of predictive models and data analytics in software engineering. Such models and analyses could be targeted at planning, design, implementation, testing, maintenance, quality assurance, evaluation, process improvement, management, decision making, and risk assessment in software and systems development. This year PROMISE received a total of 18 paper submissions. The review process was double blind and each paper was reviewed by at least three members of the program committee. An online discussion was also held for 8 days. Based on this procedure, we accepted a total of 10 full papers, which will be presented in 3 technical sessions. The acceptance criteria were entirely based on the quality of the papers, without imposing any constraint on the number of papers to be accepted.
We are delighted to announce an outstanding keynote: Release Engineering in the AI World: How can Analytics Help? By Prof. Bram Adams, Queen’s University, Canada
We would like to thank all authors for submitting high quality papers, and program committee members for their timely and accurate reviewing activity. Last, but not least, we would like to thank the FSE 2022 organizers for hosting PROMISE 2022 as a co-located event and for their logistic support in the organization of the conference.
We hope you will enjoy PROMISE 2022. We certainly will!
Many thanks from Shane McIntosh (General Chair), Gema Rodriguez-Perez and Weiyi Shang (Program Chairs).
Proceeding Downloads
Release engineering in the AI world: how can analytics help? (keynote)
The last decade, the practices of continuous delivery and deployment have taken the software engineering world by storm. While applications used to be released in an ad hoc manner, breakthroughs in (amongst others) continuous integration, ...
Improving the performance of code vulnerability prediction using abstract syntax tree information
The recent emergence of the Log4jshell vulnerability demonstrates the importance of detecting code vulnerabilities in software systems. Software Vulnerability Prediction Models (VPMs) are a promising tool for vulnerability detection. Recent studies ...
Measuring design compliance using neural language models: an automotive case study
As the modern vehicle becomes more software-defined, it is beginning to take significant effort to avoid serious regression in software design. This is because automotive software architects rely largely upon manual review of code to spot deviations ...
Feature sets in just-in-time defect prediction: an empirical evaluation
Just-in-time defect prediction assigns a defect risk to each new change to a software repository in order to prioritize review and testing efforts. Over the last decades different approaches were proposed in literature to craft more accurate ...
Profiling developers to predict vulnerable code changes
- Tugce Coskun,
- Rusen Halepmollasi,
- Khadija Hanifi,
- Ramin Fadaei Fouladi,
- Pinar Comak De Cnudde,
- Ayse Tosun
Software vulnerability prediction and management have caught the interest of researchers and practitioners, recently. Various techniques that are usually based on characteristics of the code artefacts are also offered to predict software ...
Predicting build outcomes in continuous integration using textual analysis of source code commits
Machine learning has been increasingly used to solve various software engineering tasks. One example of its usage is to predict the outcome of builds in continuous integration, where a classifier is built to predict whether new code commits will ...
LOGI: an empirical model of heat-induced disk drive data loss and its implications for data recovery
Disk storage continues to be an important medium for data recording in software engineering, and recovering data from a failed storage disk can be expensive and time-consuming. Unfortunately, while physical damage instances are well documented, ...
Assessing the quality of GitHub copilot’s code generation
The introduction of GitHub’s new code generation tool, GitHub Copilot, seems to be the first well-established instance of an AI pair-programmer. GitHub Copilot has access to a large number of open-source projects, enabling it to utilize more extensive ...
On the effectiveness of data balancing techniques in the context of ML-based test case prioritization
Regression testing is the cornerstone of quality assurance of software systems. However, executing regression test cases can impose significant computational and operational costs. In this context, Machine Learning-based Test Case Prioritization (ML-...
Identifying security-related requirements in regulatory documents based on cross-project classification
Security is getting substantial focus in many industries, especially safety-critical ones. When new regulations and standards which can run to hundreds of pages are introduced, it is necessary to identify the requirements in those documents which have ...
API + code = better code summary? insights from an exploratory study
Automatic code summarization techniques aid in program comprehension by generating a natural language summary from source code. Recent research in this area has seen significant developments from basic Seq2Seq models to different flavors of ...
Index Terms
Proceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| PROMISE | 25 | 12 | 48% |
| PROMISE 2016 | 23 | 10 | 43% |
| PROMISE '15 | 16 | 8 | 50% |
| PROMISE '14 | 21 | 9 | 43% |
| PROMISE '12 | 24 | 12 | 50% |
| PROMISE '08 | 16 | 13 | 81% |
| Overall | 125 | 64 | 51% |