ICSEA 2021: the sixteenth international conference on software engineering advances

The Sixteenth International Conference on Software Engineering Advances (ICSEA 2021), held on October 3 - 7, 2021 in Barcelona, Spain, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. The tracks treated the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learnt. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. The conference had the following tracks: Advances in fundamentals for software development Advanced mechanisms for software development Advanced design tools for developing software Software engineering for service computing (SOA and Cloud) Advanced facilities for accessing software Software performance Software security, privacy, safeness Advances in software testing Specialized software advanced applications Web Accessibility Open source software Agile and Lean approaches in software engineering Software deployment and maintenance Software engineering techniques, metrics, and formalisms Software economics, adoption, and education Business technology Improving productivity in research on software engineering Trends and achievements Similar to the previous edition, this event continued to be very competitive in its selection process and very well perceived by the international software engineering community. As such, it is attracting excellent contributions and active participation from all over the world. We were very pleased to receive a large amount of top quality contributions. We take here the opportunity to warmly thank all the members of the ICSEA 2021 technical program committee as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and efforts to contribute to the ICSEA 2021. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. This event could also not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the ICSEA 2021 organizing committee for their help in handling the logistics and for their work that is making this professional meeting a success. We hope the ICSEA 2021 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in software engineering research

Improving merging conditions for recomposing conformance checking

Efficient conformance checking is a hot topic in the field of process mining. Much of the recent work focused on improving the scalability of alignment-based approaches to support the larger and more complex processes. This is needed because process mining is increasingly applied in areas where models and logs are “big”. Decomposition techniques are able to achieve significant performance gains by breaking down a conformance problem into smaller ones. Moreover, recent work showed that the alignment problem can be resolved in an iterative manner by alternating between aligning a set of decomposed sub-components before merging the computed sub-alignments and recomposing sub-components to fix merging issues. Despite experimental results showing the gain of applying recomposition in large scenarios, there is still a need for improving the merging step, where log traces can take numerous recomposition steps before reaching the required merging condition. This paper contributes by defining and structuring the recomposition step, and proposes strategies with significant performance improvement on synthetic and real-life datasets over both the state-of-the-art decomposed and monolithic approaches

Improving merging conditions for recomposing conformance checking

\u3cp\u3eEfficient conformance checking is a hot topic in the field of process mining. Much of the recent work focused on improving the scalability of alignment-based approaches to support the larger and more complex processes. This is needed because process mining is increasingly applied in areas where models and logs are “big”. Decomposition techniques are able to achieve significant performance gains by breaking down a conformance problem into smaller ones. Moreover, recent work showed that the alignment problem can be resolved in an iterative manner by alternating between aligning a set of decomposed sub-components before merging the computed sub-alignments and recomposing sub-components to fix merging issues. Despite experimental results showing the gain of applying recomposition in large scenarios, there is still a need for improving the merging step, where log traces can take numerous recomposition steps before reaching the required merging condition. This paper contributes by defining and structuring the recomposition step, and proposes strategies with significant performance improvement on synthetic and real-life datasets over both the state-of-the-art decomposed and monolithic approaches.\u3c/p\u3