Evolution of Software SystEms and Reverse Engineering Lab

Welcome to ESSeRE, the Evolution of Software SystEms and Reverse Engineering Lab. at the University of Milano-Bicocca. Our research interests focus on the area of reverse engineering, software maintenance, software quality assessment, software architecture reconstruction, by exploiting traditional approaches and soft computing ones.


Location

Università degli Studi di Milano-Bicocca
Department of Computer Science (DISCo)
Viale Sarca, 336 - U14 Building
20126, Milano (MI), Italy

MaLTeSQuE2018: Workshop on Machine Learning Techniques for Software Quality Evaluation

MaLTeSQuE2018: Workshop on Machine Learning Techniques for Software Quality Evaluation

Co-located with SANER 2018. Campobasso, Italy, March 2018.

Co-Chairs: Francesca Arcelli Fontana, Apostolos Ampatzoglou, Fabio Palomba, Bartosz Walter.

MTD 2017: Workshop on Managing Technical Debt

MTD 2017: Workshop on Managing Technical Debt

The Ninth International Workshop on Managing Technical Debt will be held in conjunction with XP 2017 in Cologne, Germany, on May 22, 2017. http://sei.cmu.edu/community/td2017/

Co-Chairs: Francesca Arcelli Fontana, Clemente Izurieta, Robert Nord, Wolfgang Trumler.

Technical debt is a metaphor that software developers and managers increasingly use to communicate key tradeoffs related to release and quality issues. The Managing Technical Debt workshop series has, since 2010, brought together practitioners and researchers to discuss and define issues related to technical debt and how they can be studied. Workshop participants reiterate the usefulness of the concept each year, share emerging practices used in software development organizations, and emphasize the need for more research and better means for sharing emerging practices and results.

The Ninth Workshop on Managing Technical Debt will bring together leading software researchers and practitioners, especially from the area of iterative and agile software development, for the purpose of exploring theoretical and practical techniques that quantify technical debt.

The following are just some of the topics aligned with our theme:

  • techniques and tools for managing technical debt in agile, DevOps, and other software development environments
  • techniques and tools for calculating technical debt principal and interest
  • technical debt in code, design, architecture, and development and delivery infrastructure
  • measurements and metrics for technical debt
  • empirical studies on technical debt evaluations

We invite submissions of papers in any areas related to the goals of the workshop in the following categories: research papers, industrial papers, and position and future trends papers. Accepted papers will be presented at the workshop and published in the XP 2017 post-conference proceedings. For more information about submitting a paper see the Call for Papers.

Important Dates

Paper submissions: March 3, 2017 Notification of acceptance: March 24, 2017 Workshop: May 22, 2017

Organizers: Francesca, Robert, Clemente and Wolfang.

MaLTeSQuE 2017: Workshop on Machine Learning Techniques for Software Quality Evaluation

MaLTeSQuE2017: Workshop on Machine Learning Techniques for Software Quality Evaluation

Klagenfurt, February 21st, 2017

Collocated with SANER 2017.

Co-Chairs: Francesca Arcelli Fontana, Marco Zanoni, Bartosz Walter.

I. MOTIVATION

In recent years we have been observing a rising interest in adopting various approaches to exploiting machine learning (ML) and automated decision-making processes in several areas of software engineering. These models and algorithms help to reduce effort and risk related to human judgment in favor of automated systems, which are able to make informed decisions based on available data and evaluated with objective criteria. Software quality is the area that deserves particular attention. At all levels, source code quality, process quality and the quality of entire systems, researchers are still looking for new, more effective methods of evaluating various qualitative characteristics of software systems and the related processes. Human judgement is inevitable in certain areas, but is also inherently biased by implicit, subjective criteria applied in the evaluation process. Additionally, its economical effectiveness is limited, compared to automated or semi-automated approaches. Therefore, we observe a space for applying ML even more extensively than it is done currently. We also believe that applying ML can address uncertainty, in an effort to handle the size of complex systems, by supporting better code and design review, and enable automation of analyses that handle fuzzy concepts (e.g., code smells).

II. OBJECTIVE

The aim of the workshop is to provide a forum for researchers and practitioners to present and discuss new ideas, trends and results concerning applying ML to software quality evaluation. We expect that the workshop will help in (1) validation of existing and exploring new applications of ML, (2) comparing their efficiency and effectiveness, both among other automated approaches and the human judgement, and (3) adapting ML approaches already used in other areas of science.

Topics of interest include, but are not limited to:

  • Application of machine-learning in software evaluation,
  • Multi-criteria analysis of software-related data,
  • Adoption of fuzzy concept in analyzing software artifacts and processes,
  • Knowledge acquisition from software repositories,
  • Adoption and validation of machine learning models and algorithms in software engineering,
  • Decision support and analysis in software engineering,
  • Predicting models in software engineering.

IV. SUBMISSIONS

We solicit research papers and prototype demonstrations. The accepted papers would be included into the SANER 2017 proceedings and will be available all participants in advance through a workshop webpage.

Each paper will be reviewed by three PC members of the workshop. Program Committee will jointly make the final decision concerning acceptance of individual papers, based on the reviews.

The paper cannot exceed 6 pages, using the IEEE Proceedings style (same as main conference http://www.ieee.org/conferences_events/conferences/publishing/templates.html). Papers will appear in the IEEE Digital Library. Submissions can be made by the EasyChair link https://easychair.org/conferences/?conf=maltesque2017.

V. IMPORTANT DATES

  • Submission: Dec 12th, 2016
  • Notification: Dec 21th, 2016
  • Camera ready: Jan 9th, 2017
  • Workshop: Feb 21st, 2017

VI. PROGRAM COMMITTEE

- Francesca Arcelli Fontana, University of Milano-Bicocca - Alexander Chatzigeorgiou, University of Macedonia - Steve Counsell, Brunell University - Jens Dietrich, Massey University - Foutse Khomh, Polytechnique Montreal - Lech Madeyski, Wrocław University of Technology - Mirosław Ochodek, Poznań University of Technology - Haidar Osman, University of Bern - Fabio Palomba, Università di Salerno - Bartosz Walter, Poznań University of Technology - Lu Xiao, Drexel University - Aiko Yamashita, Oslo and Akershus University - Marco Zanoni, University of Milano-Bicocca

More info at http://www.cs.put.poznan.pl/maltesque/ and http://saner.aau.at/maltesque-workshop-on-machine-learning-techniques-for-software-quality-evaluation/.

Design Pattern Benchmark platform (DPB) update

The DPB platform was updated with the MARPLE results on the projects contained in PMART. The update contains results for the Singleton, Adapter, Composite, Decorator, and Factory Method design patterns.

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