Araştırma Makalesi
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Next-generation software development competencies: Identification of technical and non-technical skills needed by modern industry

Yıl 2025, Cilt: 15 Sayı: 1, 197 - 209, 15.03.2025

Öz

The software development industry is undergoing unprecedented growth and transformation, prompting a reevaluation of the skills and competencies necessary for success in this dynamic landscape. This study investigates the rapidly evolving skill requirements within the industry, driven by technological advancements. To achieve this, a Latent Dirichlet Allocation (LDA) framework is employed, enabling the identification of key topics from a dataset derived from online job postings. The analysis revealed 52 core topics pertinent to software development competencies. The findings reveal a significant emphasis on both technical domain knowledge and programming skills, with particular attention to modern programming languages such as Java, Python, and JavaScript. Moreover, non-technical skills, including communication, teamwork, and critical thinking, are underscored as vital competencies in today’s collaborative software development environments. These insights emphasize the necessity for software developers to cultivate a diverse skill set to adapt to current and future industry demands. This work serves as a crucial reference for understanding the present and future skill requirements in the software development field, providing valuable guidance for developers, employers, and educational institutions.

Kaynakça

  • Aken, A., Litecky, C., Ahmad, A., & Nelson, J. (2010). Mining for computing jobs. IEEE Software, 27(1), 78–85.
  • Barua, A., Thomas, S. W., & Hassan, A. E. (2014). What are developers talking about? An analysis of topics and trends in Stack Overflow. Empirical Software Engineering. https://doi.org/10.1007/s10664-012-9231-y
  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4–5), 993–1022. https://doi.org/10.1017/9781009218245.012
  • Burkhardt, S., & Kramer, S. (2019). Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model. Journal of Machine Learning Research, 20.
  • Chen, T. H., Thomas, S. W., & Hassan, A. E. (2016). A survey on the use of topic models when mining software repositories. Empirical Software Engineering, 21(5), 1843–1919. https://doi.org/10.1007/s10664-015-9402-8
  • De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing and Management, 54(5), 807–817. https://doi.org/10.1016/j.ipm.2017.05.004
  • Debortoli, S., Müller, O., & Vom Brocke, J. (2014). Comparing business intelligence and big data skills: A text mining study using job advertisements. Business and Information Systems Engineering, 6(5), 289–300. https://doi.org/10.1007/s12599-014-0344-2
  • Egger, R., & Yu, J. (2022). A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology, 7. https://doi.org/10.3389/fsoc.2022.886498
  • Feng, J., Zhang, Z., Ding, C., Rao, Y., Xie, H., & Wang, F. L. (2022). Context reinforced neural topic modeling over short texts. Information Sciences, 607, 79–91. https://doi.org/10.1016/j.ins.2022.05.098
  • Gurcan, F. (2023a). Identification of mobile development issues using semantic topic modeling of Stack Overflow posts. PeerJ Computer Science, 9, 1–28. https://doi.org/10.7717/peerj-cs.1658
  • Gurcan, F. (2023b). What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities. PeerJ Computer Science, 9, e1361. https://doi.org/10.7717/peerj-cs.1361
  • Gurcan, F., & Cagiltay, N. E. (2019). Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling. IEEE Access, 7, 82541–82552. https://doi.org/10.1109/ACCESS.2019.2924075
  • Gurcan, F., & Kose, C. (2017). Analysis of software engineering industry needs and trends: Implications for education. International Journal of Engineering Education, 33(4), 1361–1368.
  • McCallum, A. K. (2002). MALLET: A Machine Learning for Language Toolkit. In Http://Mallet.Cs.Umass.Edu. http://mallet.cs.umass.edu
  • Moreno, A. M., Sanchez-Segura, M. I., Medina-Dominguez, F., & Carvajal, L. (2012). Balancing software engineering education and industrial needs. Journal of Systems and Software, 85(7), 1607–1620. https://doi.org/10.1016/j.jss.2012.01.060
  • Stack Overflow. (2024). Stack Overflow Developer Jobs. https://stackoverflow.com/jobs
  • Terblanche, C., & Wongthongtham, P. (2015). Ontology-based employer demand management. Software: Practice and Experience. https://doi.org/10.1002/spe.2319
  • Thomas, S. W., Adams, B., Hassan, A. E., & Blostein, D. (2014). Studying software evolution using topic models. Science of Computer Programming, 80(PART B), 457–479. https://doi.org/10.1016/j.scico.2012.08.003
  • Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94. https://doi.org/10.1016/j.is.2020.101582
  • Wu, X., Nguyen, T., & Luu, A. T. (2024). A survey on neural topic models: methods, applications, and challenges. Artificial Intelligence Review, 57(2). https://doi.org/10.1007/s10462-023-10661-7
  • Xu, K., Lu, X., Li, Y. fang, Wu, T., Qi, G., Ye, N., Wang, D., & Zhou, Z. (2022). Neural Topic Modeling with Deep Mutual Information Estimation. Big Data Research, 30. https://doi.org/10.1016/j.bdr.2022.100344

Yeni nesil yazılım geliştirme yetkinlikleri: Modern endüstrinin ihtiyaç duyduğu teknik ve teknik olmayan becerilerin belirlenmesi

Yıl 2025, Cilt: 15 Sayı: 1, 197 - 209, 15.03.2025

Öz

Yazılım geliştirme sektörü önemli bir büyüme ve dönüşümden geçiyor ve bu dinamik ortamda başarı için gerekli becerilerin ve yeterliliklerin yeniden değerlendirilmesi gerekiyor. Bu çalışma, teknolojik gelişmelerin yönlendirdiği sektördeki hızla değişen beceri gereksinimlerini araştırıyor. Bunu başarmak için, çevrimiçi iş ilanlarından türetilen bir veri setinden temel konuların belirlenmesini sağlayan bir Gizli Dirichlet Tahsisi (Latent Dirichlet Allocation) çerçevesi kullanılıyor. Gerçekleştirilen analiz, yazılım geliştirme yetkinliklerine özgü 52 temel beceriyi ortaya çıkarmıştır. Bulgular, Java, Python ve JavaScript gibi modern programlama dillerine özel dikkat gösterilerek hem teknik alan bilgisine hem de programlama becerilerinin önemine vurgu yapmıştır. Dahası, iletişim, ekip çalışması ve eleştirel düşünme gibi teknik olmayan beceriler, günümüzün işbirlikçi yazılım geliştirme ortamlarında çok önemli yetkinlikler ortaya çıkmıştır. Bu içgörüler, yazılım geliştiricilerinin mevcut ve gelecekteki endüstri taleplerine uyum sağlamak için çeşitli bir beceri seti geliştirmeleri gerekliliğini vurgulamaktadır. Bu çalışma yazılım geliştirme alanındaki mevcut ve gelecekteki beceri gereksinimlerini anlamak için önemli bir referans görevi görerek geliştiriciler, işverenler ve eğitim kurumları için değerli rehberlik sağlamaktadır.

Kaynakça

  • Aken, A., Litecky, C., Ahmad, A., & Nelson, J. (2010). Mining for computing jobs. IEEE Software, 27(1), 78–85.
  • Barua, A., Thomas, S. W., & Hassan, A. E. (2014). What are developers talking about? An analysis of topics and trends in Stack Overflow. Empirical Software Engineering. https://doi.org/10.1007/s10664-012-9231-y
  • Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4–5), 993–1022. https://doi.org/10.1017/9781009218245.012
  • Burkhardt, S., & Kramer, S. (2019). Decoupling sparsity and smoothness in the dirichlet variational autoencoder topic model. Journal of Machine Learning Research, 20.
  • Chen, T. H., Thomas, S. W., & Hassan, A. E. (2016). A survey on the use of topic models when mining software repositories. Empirical Software Engineering, 21(5), 1843–1919. https://doi.org/10.1007/s10664-015-9402-8
  • De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing and Management, 54(5), 807–817. https://doi.org/10.1016/j.ipm.2017.05.004
  • Debortoli, S., Müller, O., & Vom Brocke, J. (2014). Comparing business intelligence and big data skills: A text mining study using job advertisements. Business and Information Systems Engineering, 6(5), 289–300. https://doi.org/10.1007/s12599-014-0344-2
  • Egger, R., & Yu, J. (2022). A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in Sociology, 7. https://doi.org/10.3389/fsoc.2022.886498
  • Feng, J., Zhang, Z., Ding, C., Rao, Y., Xie, H., & Wang, F. L. (2022). Context reinforced neural topic modeling over short texts. Information Sciences, 607, 79–91. https://doi.org/10.1016/j.ins.2022.05.098
  • Gurcan, F. (2023a). Identification of mobile development issues using semantic topic modeling of Stack Overflow posts. PeerJ Computer Science, 9, 1–28. https://doi.org/10.7717/peerj-cs.1658
  • Gurcan, F. (2023b). What issues are data scientists talking about? Identification of current data science issues using semantic content analysis of Q&A communities. PeerJ Computer Science, 9, e1361. https://doi.org/10.7717/peerj-cs.1361
  • Gurcan, F., & Cagiltay, N. E. (2019). Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling. IEEE Access, 7, 82541–82552. https://doi.org/10.1109/ACCESS.2019.2924075
  • Gurcan, F., & Kose, C. (2017). Analysis of software engineering industry needs and trends: Implications for education. International Journal of Engineering Education, 33(4), 1361–1368.
  • McCallum, A. K. (2002). MALLET: A Machine Learning for Language Toolkit. In Http://Mallet.Cs.Umass.Edu. http://mallet.cs.umass.edu
  • Moreno, A. M., Sanchez-Segura, M. I., Medina-Dominguez, F., & Carvajal, L. (2012). Balancing software engineering education and industrial needs. Journal of Systems and Software, 85(7), 1607–1620. https://doi.org/10.1016/j.jss.2012.01.060
  • Stack Overflow. (2024). Stack Overflow Developer Jobs. https://stackoverflow.com/jobs
  • Terblanche, C., & Wongthongtham, P. (2015). Ontology-based employer demand management. Software: Practice and Experience. https://doi.org/10.1002/spe.2319
  • Thomas, S. W., Adams, B., Hassan, A. E., & Blostein, D. (2014). Studying software evolution using topic models. Science of Computer Programming, 80(PART B), 457–479. https://doi.org/10.1016/j.scico.2012.08.003
  • Vayansky, I., & Kumar, S. A. P. (2020). A review of topic modeling methods. Information Systems, 94. https://doi.org/10.1016/j.is.2020.101582
  • Wu, X., Nguyen, T., & Luu, A. T. (2024). A survey on neural topic models: methods, applications, and challenges. Artificial Intelligence Review, 57(2). https://doi.org/10.1007/s10462-023-10661-7
  • Xu, K., Lu, X., Li, Y. fang, Wu, T., Qi, G., Ye, N., Wang, D., & Zhou, Z. (2022). Neural Topic Modeling with Deep Mutual Information Estimation. Big Data Research, 30. https://doi.org/10.1016/j.bdr.2022.100344
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenmesi Algoritmaları, Veri Analizi
Bölüm Makaleler
Yazarlar

Fatih Gürcan 0000-0001-9915-6686

Cemal Köse 0000-0002-5982-4771

Yayımlanma Tarihi 15 Mart 2025
Gönderilme Tarihi 12 Aralık 2024
Kabul Tarihi 14 Şubat 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 1

Kaynak Göster

APA Gürcan, F., & Köse, C. (2025). Next-generation software development competencies: Identification of technical and non-technical skills needed by modern industry. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 15(1), 197-209. https://doi.org/10.17714/gumusfenbil.1600286