Detecting Emergent Behaviors and Implied Scenarios in Scenario-Based Specifications: A Machine Learning Approach

Jahan, Munima; Abad, Zahra Shakeri Hossein; Far, Behrouz H.

doi:10.1109/mise.2019.00009

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…B. DeVries et al [45] have developed Automatic Detection of Incomplete Requirements via Symbolic Analysis. M. Jahan et al [17] have implied a machine learning approach to detecting unexpected scenarios in software requirements modeling. Implementation of last three contributions could be potentially applied as preprocessors of Procrust.…”

Section: Ar Tool Functionalitymentioning

confidence: 99%

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Initial training in design of computer programs using UML

Andreyanov,

Gagarin

2023

P Sci Edu

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Introduction. Design is commonly acknowledged as a key process in the life cycle of computer programs and softwareintensive systems. The process efficaciously reveals creative capabilities of the author programmer and predetermines the basic advantages as well as shortcomings of the resulting program product, service or system. Design automation and other kinds of its support have been emerged simultaneously with onset of the software industry. From the beginning of the century the MDA (Model Driven Architecture) approach grew famous, offering to comprehend a design of a program as a process of sequential transformation of the program’s models, represented in UML or in other languages related to UML. Without becoming dominant, the MDA approach successfully evolves inside of a broader approach called MDE (Model Driven Engineering). Mastering UML and understanding of its role in MDE appears to be a prerequisite for a seamless entering of a professional programmer into domain of engineering of program systems. This provision justifies an increased attention to methods of UML teaching and to peculiarities of UML learning, and, moreover, to the professional education in the area of programs and program systems design technologies. This paper is aimed to draw attentions to challenges of the initial training in UML and tackle the challenges contributing a methodology and tools for sake of fostering the quality of education in program design. Materials and methods. The research is based on the university syllabi, the author’s lecture notes on software engineering, reports on laboratory works and course papers, protocols of experimental and sample runs of the developed programs, scientific publications, including pedagogical and engineering periodicals, conference materials, Internet resources, such as sites of the ACM, the OMG and others. The following research methods were used: system analysis of the own experience in program design as well as experience remarkable in student’s works; theoretical conceptual modelling of the program design including modelling of design scenarios; a trial implementation of the developed algorithms. Research results. A conceptual cognitive model of design as of a process during program development is proposed, highlighting the designer’s behavior when dealing with project artefacts. Feasible scenarios of analytic and advisory intervention into the design process are specified, using the model. A sample architecture of a program tool suitable for such an intervention is proposed as well as algorithms that enable online critical evaluation of the project’s current state, prompting feasible tracks of the project’s evolvement, revealing designer’s ideas and pointing some UML misuse. The algorithms are built in a program tool named Procrust that implements the proposed architecture. Trials of Procrust show that it basically fits to primary training of software design using UML and, in particular, to a training in the MDA/MDE methodology. Conclusion. The research results enable to set up an environment for comfortable and efficient program design using UML due to employing the proposed analytic and advisory support of the design process. Algorithms of the support and supporting program tools are targeted at initial design training on curricula of higher school and secondary vocational education. The algorithms foster in-depth study of UML and acquiring of advanced software design skills.

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Section: Ar Tool Functionalitymentioning

confidence: 99%

“…These are: attempts of M. Méré et al [15] to apply formal verification methods to UML models, application of a machine learning to forecast of design operation by J. Di Rocco et al [16] and detecting emergent behaviors in scenariobased specifications of programs by M. Jahan et al [17].…”

Section: Introductionmentioning

confidence: 99%

Initial training in design of computer programs using UML

Andreyanov,

Gagarin

2023

P Sci Edu

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“…Other approaches suggest what information is missing in the scenarios by generating combinations of new scenarios based on existing ones. Nevertheless, the experts still need to analyze the suggestions to identify if they are useful and realistic or not, which is critical and demands a lot of effort [12]. Makino et al [18] present a method to generate alternative/exceptional scenarios using differential information of existing scenarios.…”

Section: Related Workmentioning

confidence: 99%

Anais do Workshop em Engenharia de Requisitos

2022

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Requirements engineering is one of the most important stages in software development. Errors committed at this stage require a lot of effort to fix in further stages. There are two main strategies to deal with software development: classic and agile. Classic software development relies on an extensive and very detailed specification, while agile development minimizes the effort on requirements specifications, relying on brief descriptions and a running prototype as a means of communication. Before the specification of requirements, there are some preliminary meetings held between the stakeholders and the IT team with the objective of discovering the goal and scope of the application to be developed. The knowledge transferred during these meetings is crucial to provide a context to requirements, no matters whether the approach is classic or agile. We argue that scenarios written in natural language are adequate artifacts to capture this knowledge. This paper proposes an iterative and collaborative approach to describe scenarios. The approach has two main activities: one of them is concerned with the description that stakeholders should perform, while the other provides guidelines to verify and possible improve the scenarios. This paper also presents a prototype tool that helps enforcing these guidelines automatically. This prototype relies on natural language processing. Finally, the paper shows the result of a preliminary evaluation of the proposed approach that indicates its results are promising

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“…In the requirements stage, writing requirements specifications is highly deemed to be a human-centric task. Prior work by Pandita et al [50] and Jahan et al [51] have inferred the most probable specifications and identified its unexpected behaviors from various artifacts by employing ML techniques, respectively. Ferrari et al [52] identified ambiguous requirements among different domains using ML.…”

Section: Applications Of ML Aiming At Software Maintenancementioning

confidence: 99%

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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Detecting Emergent Behaviors and Implied Scenarios in Scenario-Based Specifications: A Machine Learning Approach

Cited by 7 publications

References 18 publications

Initial training in design of computer programs using UML

Initial training in design of computer programs using UML

Anais do Workshop em Engenharia de Requisitos

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

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