Retrosynthesis from transforms to predictive sustainable chemistry and nanotechnology: a brief tutorial review

Runtime Verification can be defined as a collection of formal methods for studying the dynamic evaluation of execution traces against formal specifications. Aside from creating a monitor from specifications and building algorithms for the evaluation of the trace, the process of gathering events and making them available for the monitor and the communication between the system under analysis and the monitor are critical and important steps in the runtime verification process. In many situations and for a variety of reasons, the event trace could be incomplete or could contain imprecise events. When a missing or ambiguous event is detected, the monitor may be unable to deliver a sound verdict. In this survey, we review the literature dealing with the problem of monitoring with incomplete traces. We list the different causes of uncertainty that have been identified, and analyze their effect on the monitoring process. We identify and compare the different methods that have been proposed to perform monitoring on such traces, highlighting the advantages and drawbacks of each method. 

Uncertainty in runtime verification: A survey

A Method to Identify Overfitting Program Repair Patches Based on Expression Tree

Industrial robotic systems (IRS) consist of industrial robots that automate industrial processes. They accurately perform repetitive tasks, replacing or assisting with dangerous jobs like assembly in the automotive and chemical industries. Failures in these systems can be catastrophic, so it is important to ensure their quality and safety before using them. One way to do this is by applying a software testing process to find faults before they become failures. However, software testing in industrial robotic systems has some challenges. These include differences in perspectives on software testing from people with diverse backgrounds, coordinating and collaborating with diverse teams, and performing software testing within the complex integration inherent in industrial environments. In traditional systems, a well-known development process uses simple, structured sentences in English to facilitate communication between project team members and business stakeholders. This process is called behavior-driven development (BDD), and one of its pillars is the use of templates to write user stories, scenarios, and automated acceptance tests. We propose a software testing (ST) approach called automated acceptance testing for industrial robotic systems (AAT4IRS) that uses natural language to write the features and scenarios to be tested. We evaluated our ST approach through a proof-of-concept, performing a pick-and-place process and applying mutation testing to measure its effectiveness. The results show that the test suites implemented using AAT4IRS were highly effective, with 79% of the generated mutants detected, thus instilling confidence in the robustness of our approach. 

AAT4IRS: automated acceptance testing for industrial robotic systems

The detection of faults in software systems can be enhanced effectively by model-based mutation testing. The efficiency of this technique is hindered when mutants are equivalent to the original system model, since this makes them useless. Recently, the application of model-based mutation testing to real-time systems modelled as timed games has been investigated, which has resulted in guidelines for statically avoiding equivalent mutants.In this paper, we recast this problem into the framework of parametric timed games. We then prove a correspondence between theoretical results for the detection of equivalent mutants in timed games and the property of monotonicity that is known to hold for a sub-class of parametric timed games called L/U parametric timed games. The presented results not only simplify the theory underlying the detection of equivalent mutants in timed games, but at the same time they improve the expressiveness of a known decidable fragment of parametric timed games for which monotonicity holds. 

Mutant Equivalence as Monotonicity in Parametric Timed Games

Boolean test input generation is the process of finding sets of values for variables of a logical expression such that a given coverage criterion is achieved. This paper presents a formal framework in which evaluating an expression produces a tree structure, and where a coverage criterion is expressed as equivalence classes induced by a particular transformation over these trees. It then defines many well-known coverage criteria as particular cases of this framework. The paper describes an algorithm to generate test suites by a reduction through a graph problem; this algorithm works in the same way regardless of the criterion considered. An experimental evaluation of this technique shows that it produces test suites that are in many cases smaller than existing tools.

Test Suite Generation for Boolean Conditions with Equivalence Class Partitioning

Synthia is a versatile, modular and extensible Java-based data structure generation library. It is centered on the notion of “pickers”, which are objects producing values of a given type on demand. Pickers are stateful and can be given as input to other pickers; this chaining principle can generate objects whose structure follows a complex pattern. The paper describes the core principles and key features of the library, including test input shrinking, provenance tracking, and object mutation.

/pdf/synthia-a-generic-and-flexible-data-structure-generator-14eoezzo.pdf

Synthia: a Generic and Flexible Data Structure Generator

Synthia is a versatile, modular and extensible Java-based data structure generation library. It is centered on the notion of "pickers", which are objects producing values of a given type on demand. Pickers are stateful and can be given as input to other pickers; this chaining principle can generate objects whose structure follows a complex pattern. The paper describes the core principles and key features of the library, including test input shrinking, provenance tracking, and object mutation. 

Synthia

Context: There are several initiatives to semi-automate the initial selection of studies task for Systematic Literature Reviews (SLR) to reduce effort and potential bias. Objective: We propose a strategy called SCAS-AI to semi-automate the initial selection task. This strategy improves the original SCAS strategy with Artificial Intelligence (AI) resources (fuzzy logic and genetic algorithm) for studies selection. Method: We evaluated the SCAS-AI strategy through a quasi-experiment with SLRs in Software Engineering (SE). Results: In general, the SCAS-AI strategy improved the results achieved using the original SCAS strategy in reducing the effort of the initial selection task. The effort reduction applying SCAS-AI was 39.1%. In addition, the errors percentage was 0.3% for studies automatically excluded (false negative – loss of evidence) and 3.3% for studies automatically included (false positive – evidence later excluded during the full-text reading). Conclusion: The results show the potential of the investigated AI techniques to support the initial selection task for SLRs in SE.

SCAS-AI: A Strategy to Semi-Automate the Initial Selection Task in Systematic Literature Reviews

A modular pipeline for enforcement of security properties at runtime

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sylvain Hallé

Author Tools

Chat about Author