Automated conflict resolution between multiple clinical pathways:A technology report

Background The number of people in the UK with three or more long-term conditions continues to grow and the management of patients with co-morbidities is complex. In treating patients with multimorbidities, a fundamental problem is understanding and detecting points of conflict between different guidelines which to date has relied on individual clinicians collating disparate information. Objective We will develop a framework for modelling a diverse set of care pathways, and investigate how conflicts can be detected and resolved automatically. We will use this knowledge to develop a software tool for use by clinicians that can map guidelines, highlight root causes of conflict between these guidelines and suggest ways they might be resolved. Method Our work consists of three phases. First, we will accurately model clinical pathways for six of the most common chronic diseases; second, we will automatically identify and detect sources of conflict across the pathways and how they might be resolved. Third, we will present a case study to prove the validity of our approach using a team of clinicians to detect and resolve the conflicts in the treatment of a fictional patient with multiple common morbidities and compare their findings and recommendations with those derived automatically using our novel software. Discussion This paper describes the development of an important software-based method for identifying a conflict between clinical guidelines. Our findings will support clinicians treating patients with multimorbidity in both primary and secondary care settings

A design pattern for optimizations in data intensive applications using ABS and JAVA 8

Cloud environments have become a standard method for enterprises to offer their applications by means of web services, data management systems, or simply renting out computing resources. In our previous work, we presented how we can use a modeling language together with the new features of JAVA 8 to overcome certain drawbacks of data structures and synchronization mechanisms in parallel applications. We extend this solution into a design pattern that allows application-specific optimizations in a distributed setting. We validate this integration using our previous case study of the Prime Sieve of Eratosthenes and illustrate the performance improvements in terms of speed-up and memory consumption

A design pattern for optimizations in data intensive applications using ABS and JAVA 8

Cloud environments have become a standard method for enterprises to offer their applications by means of web services, data management systems, or simply renting out computing resources. In our previous work, we presented how we can use a modeling language together with the new features of JAVA 8 to overcome certain drawbacks of data structures and synchronization mechanisms in parallel applications. We extend this solution into a design pattern that allows application-specific optimizations in a distributed setting. We validate this integration using our previous case study of the Prime Sieve of Eratosthenes and illustrate the performance improvements in terms of speed-up and memory co

SkipAnalyzer: A Tool for Static Code Analysis with Large Language Models

We introduce SkipAnalyzer, a large language model (LLM)-powered tool for static code analysis. SkipAnalyzer has three components: 1) an LLM-based static bug detector that scans source code and reports specific types of bugs, 2) an LLM-based false-positive filter that can identify false-positive bugs in the results of static bug detectors (e.g., the result of step 1) to improve detection accuracy, and 3) an LLM-based patch generator that can generate patches for the detected bugs above. As a proof-of-concept, SkipAnalyzer is built on ChatGPT, which has exhibited outstanding performance in various software engineering tasks. To evaluate SkipAnalyzer, we focus on two types of typical and critical bugs that are targeted by static bug detection, i.e., Null Dereference and Resource Leak as subjects. We employ Infer to aid the gathering of these two bug types from 10 open-source projects. Consequently, our experiment dataset contains 222 instances of Null Dereference bugs and 46 instances of Resource Leak bugs. Our study demonstrates that SkipAnalyzer achieves remarkable performance in the mentioned static analysis tasks, including bug detection, false-positive warning removal, and bug repair. In static bug detection, SkipAnalyzer achieves accuracy values of up to 68.37% for detecting Null Dereference bugs and 76.95% for detecting Resource Leak bugs, improving the precision of the current leading bug detector, Infer, by 12.86% and 43.13%, respectively. For removing false-positive warnings, SkipAnalyzer can reach a precision of up to 93.88% for Null Dereference bugs and 63.33% for Resource Leak bugs. Additionally, SkipAnalyzer surpasses state-of-the-art false-positive warning removal tools. Furthermore, in bug repair, SkipAnalyzer can generate syntactically correct patches to fix its detected bugs with a success rate of up to 97.30%

Generalized Property-Directed Reachability for Hybrid Systems

Generalized property-directed reachability (GPDR) belongs to the family of the model-checking techniques called IC3/PDR. It has been successfully applied to software verification; for example, it is the core of Spacer, a state-of-the-art Horn-clause solver bundled with Z3. However, it has yet to be applied to hybrid systems, which involve a continuous evolution of values over time. As the first step towards GPDR- based model checking for hybrid systems, this paper formalizes HGPDR, an adaptation of GPDR to hybrid systems, and proves its soundness. We also implemented a semi-automated proof-of-concept verifier, which allows a user to provide hints to guide verification steps.Comment: To appear in VMCAI 202

On verifying ATL transformations using 'off-the-shelf' SMT solvers

International audienceMDE is a software development process where models constitute pivotal elements of the software to be built. If models are well-specified, transformations can be employed for various purposes, e.g., to produce final code. However, transformations are only meaningful when they are 'correct': they must produce valid models from valid input models. A valid model has conformance to its meta-model and fulfils its constraints, usually written in OCL. In this paper, we propose a novel methodology to perform automatic, unbounded verification of ATL transformations. Its main component is a novel first-order semantics for ATL transformations, based on the interpretation of the corresponding rules and their execution semantics as first-order predicates. Although, our semantics is not complete, it does cover a significant subset of the ATL language. Using this semantics, transformation correctness can be automatically verified with respect to non-trivial OCL pre- and postconditions by using SMT solvers, e.g. Z3 and Yices