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Article Citation - WoS: 9Citation - Scopus: 11Industry Oriented Advanced Software Engineering Education Curriculum(Fac Teacher Education, 2012) Mishra, Alok; Mishra, Deepti; Computer Engineering; Software EngineeringSoftware engineering is the fastest-evolving engineering discipline and most of the tasks of software development organizations are diverse in nature. Various studies have shown that there is a wide gap between software industry needs and education for prospective software engineers. It is the responsibility of Software engineering education to prepare SE professionals by providing them with the skills to meet the expectations of the software industry. SE curriculum should correspond to the industry needs, and only then can Universities produce highly skilled professionals, who can meet the needs of software industry. During the last decade, software engineering education (SEE) has been emerging as an independent and mature discipline. Accordingly, various studies are being conducted to provide guidelines for SEE curriculum design. This paper summarizes the need for software industry related courses and discusses the significance of industry oriented software engineering education to meet the educational objectives of all stakeholders. The software industry oriented curriculum for undergraduate and graduate levels is discussed. An industry oriented graduate level (master's level) software engineering course which includes foundational and applied courses to provide effective training for future software engineers is also proposed. This will lead to an increase in their employment prospects in the industrial and allied sectors.Article Citation - WoS: 9Citation - Scopus: 17An Assessment of the Software Engineering Curriculum in Turkish Universities: Ieee/Acm Guidelines Perspective(Fac Teacher Education, 2011) Mishra, Alok; Yazici, Ali; Software EngineeringSoftware engineering (SE) education has been emerging as an independent and mature discipline. Accordingly, various studies are being done to provide guidelines for SE education curriculum design. This paper presents software engineering education evolvement in Turkey, present SE education scenario in different universities along with the significance of software technology parks in relevance to software engineering education. The objective of this paper is to provide an assessment of SE curriculum in Turkish Universities with respect to IEEE/ACM guidelines given in SEEK (2004). This study will provide a guideline to universities conducting an SE programme at undergraduate level to align their course curriculum with IEEE/ACM guidelines (SEEK, 2004).Conference Object Citation - WoS: 3Citation - Scopus: 5Predicting Software Functional Size Using Natural Language Processing: an Exploratory Case Study(IEEE, 2024) Unlu, Huseyin; Tenekeci, Samet; Ciftci, Can; Oral, Ibrahim Baran; Atalay, Tunahan; Hacaloglu, Tuna; Demirors, OnurSoftware Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and Software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.
