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ESEM '18- Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Full Citation in the ACM Digital Library

SESSION: Full research papers

Architecture, technologies and challenges for cyber-physical systems in industry 4.0: a systematic mapping study

Florian Hofer

Background: The vision of a fourth industrial revolution lately strongly captured the attention of research A Cyber-physical system (CPS) is one of the main drivers of this vision. Such system controls an underlying factory interacting with sensors, actuators and other systems creating systems-of-systems. A main point of interest is how these components are built and interconnected, i.e. the system's architecture, and how it might be improved to increase reliability and security.

ESEM '20: Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

Full Citation in the ACM Digital Library

SESSION: Technical Papers

A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects

Andrew J. Simmons
Scott Barnett
Jessica Rivera-Villicana
Akshat Bajaj
Rajesh Vasa

Background: Meeting the growing industry demand for Data Science requires cross-disciplinary teams that can translate machine learning research into production-ready code. Software engineering teams value adherence to coding standards as an indication of code readability, maintainability, and developer expertise. However, there are no large-scale empirical studies of coding standards focused specifically on Data Science projects. Aims: This study investigates the extent to which Data Science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? Method: We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity. Results: Data Science projects suffer from a significantly higher rate of functions that use an excessive numbers of parameters and local variables. Data Science projects also follow different variable naming conventions to non-Data Science projects. Conclusions: The differences indicate that Data Science codebases are distinct from traditional software codebases and do not follow traditional software engineering conventions. Our conjecture is that this may be because traditional software engineering conventions are inappropriate in the context of Data Science projects.

FairWare '18- Proceedings of the International Workshop on Software Fairness

Full Citation in the ACM Digital Library

SESSION: Fairness definitions and guarantees

Fairness definitions explained

Sahil Verma
Julia Rubin

Algorithm fairness has started to attract the attention of researchers in AI, Software Engineering and Law communities, with more than twenty different notions of fairness proposed in the last few years. Yet, there is no clear agreement on which definition to apply in each situation. Moreover, the detailed differences between multiple definitions are difficult to grasp. To address this issue, this paper collects the most prominent definitions of fairness for the algorithmic classification problem, explains the rationale behind these definitions, and demonstrates each of them on a single unifying case-study. Our analysis intuitively explains why the same case can be considered fair according to some definitions and unfair according to others.

FormaliSE 2014- Proceedings of the 2nd FME Workshop on Formal Methods in Software Engineering

Full Citation in the ACM Digital Library

SESSION: Invited Talk

Software reliability via machine learning (invited talk)

Aditya V. Nori

SESSION: Integration of Formal Methods in Software Engineering I

Formal specification comprehension: the art of reading and writing z

Andreas Bollin
Dominik Rauner-Reithmayer

SESSION: Integration of Formal Methods in Software Engineering II

Flexible modular formalization of UML sequence diagrams

Luciano Baresi
Mohammad Mehdi Pourhashem Kallehbasti
Matteo Rossi

From an abstract specification in event-b toward an UML/OCL model

Imen Sayar
Mohamed Tahar Bhiri

Generating supportive hypotheses in introducing formal methods using a software processes improvement model

Shigeru Kusakabe
Hsin-Hung Lin
Yoichi Omori
Keijiro Araki

SESSION: Analysis and Verification

Using mCRL2 for the analysis of software product lines

Maurice H. ter Beek
Erik P. de Vink

Compiling polychronous programs into conditional partial orders for ASIP synthesis

Mahesh Nanjundappa
Sandeep K. Shukla

SESSION: Verification and Testing

Formalization of software models for cyber-physical systems

Sandeep Neema
Gabor Simko
Tihamer Levendovszky
Joseph Porter
Akshay Agrawal
Janos Sztipanovits

Analysis and testing of PLEXIL plans

Jason Biatek
Michael W. Whalen
Mats P. E. Heimdahl
Sanjai Rayadurgam
Michael R. Lowry

FormaliSE '16- Proceedings of the 4th FME Workshop on Formal Methods in Software Engineering

Full Citation in the ACM Digital Library

Deductive evaluation: formal code analysis with low user burden

Ben L. Di Vito

Undertaking the tokeneer challenge in event-b

Víctor Rivera
Sukriti Bhattacharya
Néstor Cataño

Simple synthesis of reactive systems with tolerance for unexpected environmental behavior

Shigeki Hagihara
Atsushi Ueno
Takashi Tomita
Masaya Shimakawa
Naoki Yonezaki

Download malware? no, thanks: how formal methods can block update attacks

Francesco Mercaldo
Vittoria Nardone
Antonella Santone
Corrado Aaron Visaggio

Validating formal specifications using testing-based specification animation

Shaoying Liu

Towards synthesis from assume-guarantee contracts involving infinite theories: a preliminary report

Andreas Katis
Andrew Gacek
Michael W. Whalen

Toward rigorous design of domain-specific distributed systems

Mohammed Al-Mahfoudh
Ganesh Gopalakrishnan
Ryan Stutsman

FormaliSE '18- Proceedings of the 6th Conference on Formal Methods in Software Engineering

Full Citation in the ACM Digital Library

SESSION: Keynote

Risk management for high tech systems

Marielle Stoelinga

How do we ensure that self-driving cars, nuclear power plants and Internet-of-things devices are safe and reliable? That is the topic of risk management. Fault tree analysis is a very popular technique here, deployed by many institutions like NASA, ESA, Honeywell, Ford, Airbus, the FDA, Toyota, Shell etc.

FormaliSE '20: Proceedings of the 8th International Conference on Formal Methods in Software Engineering

Full Citation in the ACM Digital Library

SESSION: Regular Papers

Active Learning of Decomposable Systems

Omar al Duhaiby
Jan Friso Groote

Active automata learning is a technique of querying black box systems and modelling their behaviour. In this paper, we aim to apply active learning in parts. We formalise the conditions on systems---with a decomposable set of actions---that make learning in parts possible. The systems are themselves decomposable through non-intersecting subsets of actions. Learning these subsystems/components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry.

FoSADA '15: Proceedings of the 1st International Workshop on Future of Software Architecture Design Assistants

Full Citation in the ACM Digital Library