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Comparing the understandability of iteration mechanisms over Collections in Java

Hnatkowska Bogumiła, Krych Bartosz

Foundations of Computing and Decision Sciences

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2023

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Vol. 48, No. 1

19--37

EN

Source code understandability is a desirable quality factor affecting long-term code maintenance. Understandability of source code can be assessed in a variety of ways, including subjective evaluation of code fragments (perceived understandability), correctness, and response time to tasks performed. It can also be assessed using various source code metrics, such as cyclomatic complexity or cognitive complexity. Programming languages are evolving, giving programmers new ways to do the same things, e.g., iterating over collections. Functional solutions (lambda expressions and streams) are added to typical imperative constructs like iterators or for-each statements. This research aims to check if there is a correlation between perceived understandability, understandability measured by task correctness, and predicted by source code metrics for typical tasks that require iteration over collections implemented in Java. The answer is based on the results of an experiment. The experiment involved 99 participants of varying ages, declared Java knowledge and seniority measured in years. Functional code was perceived as the most understandable, but only in one case, the subjective assessment was confirmed by the correctness of answers. In two examples with the highest perceived understandability, streams received the worst correctness scores. Cognitive complexity and McCabe's complexity had the lowest values in all tasks for the functional approach, but - unfortunately - they did not correlate with answer correctness. The main finding is that the functional approach to collection manipulation is the best choice for the filter-map-reduce idiom and its alternatives (e.g., filter-only). It should not be used in more complex tasks, especially those with higher complexity metrics.

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A reusability-oriented use-case model specification language

Hnatkowska Bogumiła, Zabawa Piotr

Annals of Computer Science and Information Systems

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2023

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Vol. 35

567--576

EN

Use-case models play an important role in the software development processes. They are used for specifying functional requirements, estimating software development project efforts, and planning iterations. The use-case model is subject to change as requirements are modified or the model is refactored. Therefore, it is essential that the use-case model is not redundant and reusable. Existing approaches for use-case model specification support reusability in a limited way. This paper fills the gap. It introduces a new Use-Case Flow (UCF) approach to specify the entire use-case model in a convenient yet formalized way. The paper contains the UCF metamodel, consisting of its abstract syntax and a description of the semantics of the metamodel elements. A textual concrete syntax of the T-UCF is also introduced and informally described. The concrete syntax is dedicated to the specification of the use-case model in a well-defined textual form. The stress is put on the reusability of the constructs defined by a use-case modeler when defining or changing the use-case model. An example of a usecase model specified in the T-UCF language is presented in the paper.

3

Extracting class diagram from hidden dependencies in data set

Hnatkowska Bogumiła, Huzar Zbigniew, Tuzinkiewicz Lech

Computer Science

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2020

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T. 21 (2)

211--236

EN

A conceptual model is a high-level, graphical representation of a specic domain, presenting its key concepts and relationships between them. In particular, these dependencies can be inferred from concepts' instances being a part of big raw data files. The paper aims to propose a method for constructing a conceptual model from data frames encompassed in data files. The result is presented in the form of a class diagram. The method is explained with several examples and verified by a case study in which the real data sets are processed. It can also be applied for checking the quality of a data set.