Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Software Change Size Measurement: an Exploratory Systematic Mapping Study(CEUR-WS, 2024) Hacaloglu, T.; Demirörs, Onur; Küçükateş Ömüral, N.; Kılınç Soylu, G.; Demirörs, O.Change in software projects can occur through various channels. Customers may request modifications or new features; appraisal activities such as reviews or testing may uncover issues that necessitate adjustments, or products may need to adapt to changes in their operating environment. Therefore, it is essential to assess these changes explicitly and objectively within the scope of software engineering activities. Specifically, quantifying change by measuring its size is crucial for successful management, as without a meaningful metric, it is impossible to accurately assess its impact on the project's effort, schedule, and cost. This study aims to explore the concept of change in software engineering literature, with a particular emphasis on the methods used to measure its size. The study reveals that the current literature on this topic is still in its early stages and the measurement and estimation of changes remain challenging throughout both development and maintenance phases. According to the reviewed articles, size is primarily used for effort estimation. Various software artifacts from different stages of the Software Development Life Cycle (SDLC) serve as input for change measurement, highlighting the need for a versatile size measurement applicable across all SDLC phases. Most of the reviewed articles interpret change in the context of maintenance activities. This research sets a benchmark for the status of software size measures for software change and highlights related problems to suggest further research topics. © 2024 Copyright for this paper by its authors.Conference Object Citation - Scopus: 3Predicting Software Size and Effort From Code Using Natural Language Processing(CEUR-WS, 2024) Tenekeci, S.; Demirörs, Onur; Ünlü, H.; Dikenelli, E.; Selçuk, U.; Kılınç Soylu, G.; Demirörs, O.Software Size Measurement (SSM) holds a crucial role in software project management by facilitating the acquisition of software size, which serves as the primary input for development effort and schedule estimation. However, many small and medium-sized companies encounter challenges in conducting objective SSM and Software Effort Estimation (SEE) due to resource constraints and a lack of expert workforce. This often leads to inaccurate estimates and projects exceeding planned time and budget. Hence, organizations need to perform objective SSM and SEE with minimal resources and without relying on an expert workforce. In this research, we introduce two exploratory case studies aimed at predicting the functional size (COSMIC and Event-based size) and effort of software projects from the code using a deep-learning-based NLP model: CodeBERT. For this purpose, we collected and annotated two datasets consisting of 4800 Python and 1100 C# functions. Then, we trained a classification model to predict COSMIC data movements (entry, exit, read, write) and four regression models to predict Event-based size (interaction, communication, process) and effort. Despite utilizing a relatively small dataset for model training, we achieved promising results with an 84.5% accuracy for the COSMIC size, 0.13 normalized mean absolute error (NMAE) for the Event-based size, and 0.18 NMAE for the effort. These findings are particularly insightful as they demonstrate the practical utility of language models in SSM and SEE. © 2024 Copyright for this paper by its authors.Review Citation - WoS: 9Citation - Scopus: 12Readiness and Maturity Models for Industry 4.0: a Systematic Literature Review(John Wiley and Sons Ltd, 2023) Ünlü, H.; Demirörs, O.; Garousi, V.Industry 4.0 changes traditional manufacturing relationships from isolated optimized cells to fully integrated data and product flows across borders with its technological pillars. However, the transition to Industry 4.0 is not a straightforward journey in which organizations need assistance. A well-known approach that can be utilized during the early phases of the transition is to assess the capability of the organization. Maturity models are frequently used to improve capability. In this systematic literature review (SLR), we analyzed 22 maturity and readiness models based on 10 criteria: year, type, focus, structure, research methodology followed during the design of models, base frameworks, tool support, community support, objectivity, and extent of usage in practice. Our SLR provides a well-defined comparison for organizations to choose and apply available models. This SLR showed that (1) there is no widely accepted maturity/readiness model for Industry 4.0, as well as no international standard; (2) only a few models have received positive feedback from the industry, whereas most do not provide any practical usage information; and (3) the objectivity of the assessment method is controversial in most of the models. We have also identified a number of issues as open research areas for assessing readiness and maturity models for Industry 4.0. © 2023 John Wiley & Sons, Ltd.