TalentJobRadar: Advanced Data-Driven Recommendations for In-Demand QA Soft Skills and Career Opportunities
Resumo
Introduction: Software Quality Assurance (QA) is essential to ensure that software products meet predefined requirements. While QA tasks are technical, soft skills play a crucial role in project success, product quality, and the productivity of QA professionals. Objectives: The main objective of this work is to provide a job and skill recommendation tool focused on the Brazilian QA market. Methods: Data was extracted from 2,164 LinkedIn job postings using a data-driven, inductive approach, combining both manual and automated processes. Job descriptions and users’ skills were mapped into binary vectors for comparison. The tool displays job recommendations in a card format, showing company name, required skills, LinkedIn links, and the user’s suitability for the position. Additionally, it suggests skills for improvement and highlights the top three skills associated with the user’s current soft skills, presenting a radar chart that shows job availability by seniority level. Evaluation: We conducted a preliminary evaluation of the tool using 45 synthetic profiles representing varying skill levels to simulate diverse user scenarios, allowing us to assess the system’s adaptability and effectiveness. Job recommendations demonstrated notable precision, recall, and F1-score values, while skill recommendations showed positive results in terms of relative coverage, precision, and relevance. However, applying filters led to a decrease in overall metrics, highlighting limitations in filtered recommendations. Conclusion: The tool shows promise in helping QA professionals at various experience levels identify relevant job opportunities and areas for skill enhancement. Although filtered recommendations present limitations, the system effectively highlights suitable positions and skills for development, supporting employability in the QA field. Future improvements include refining filtered recommendation accuracy and expanding to technical skill recommendations.
Referências
Charu C Aggarwal et al. 2016. Recommender systems. Vol. 1. Springer.
Rishabh Ahuja, Arun Solanki, and Anand Nayyar. 2019. Movie recommender system using k-means clustering and k-nearest neighbor. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 263–268.
K Alex. 2009. Soft skills. S. Chand Publishing.
Azuraini Abu Bakar and Choo-Yee Ting. 2011. Soft skills recommendation systems for IT jobs: A Bayesian network approach. In 2011 3rd Conference on Data Mining and Optimization (DMO). IEEE, 82–87.
Randy Bancino and Claire Zevalkink. 2007. Soft skills: the new curriculum for hard-core technical professionals. Techniques: Connecting Education and Careers (J1) 82, 5 (2007), 20–22.
Justin Basilico and Thomas Hofmann. 2004. Unifying collaborative and content-based filtering. In Proceedings of the twenty-first international conference on Machine learning. 9.
Wicher Bergsma. 2013. A bias-correction for Cramér’s V and Tschuprow’s T. Journal of the Korean Statistical Society 42, 3 (2013), 323–328.
Giulia Bruno, Tania Cerquitelli, Silvia Chiusano, and Xin Xiao. 2014. A clusteringbased approach to analyse examinations for diabetic patients. In 2014 IEEE International Conference on healthcare informatics. IEEE, 45–50.
Luiz Fernando Capretz and Faheem Ahmed. 2010. Why do we need personality diversity in software engineering? ACM SIGSOFT Software Engineering Notes 35, 2 (2010), 1–11.
Oualid Chenni, Yanis Bouda, Hamid Benachour, and Chahnez Zakaria. 2015. A content-based recommendation approach using semantic user profile in e-recruitment. In Theory and Practice of Natural Computing: Fourth International Conference, TPNC 2015, Mieres, Spain, December 15-16, 2015. Proceedings 4. Springer, 23–32.
Maya Daneva, Chong Wang, and Patrick Hoener. 2017. What the Job Market Wants from Requirements Engineers? An Empirical Analysis of Online Job Ads from the Netherlands. In ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). ACM, 448–453.
Mehdi Elahi, Francesco Ricci, and Neil Rubens. 2016. A survey of active learning in collaborative filtering recommender systems. Computer Science Review 20 (2016), 29–50.
Silvia Fareri, Nicola Melluso, Filippo Chiarello, and Gualtiero Fantoni. 2021. SkillNER: Mining and mapping soft skills from any text. Expert Systems with Applications 184 (2021), 115544.
Mauricio Noris Freire and Leandro Nunes de Castro. 2021. e-Recruitment recommender systems: a systematic review. Knowledge and Information Systems 63 (2021), 1–20.
Matthias Galster, Antonija Mitrovic, Sanna Malinen, and Jay Holland. 2022. What soft skills does the software industry really want? An exploratory study of software positions in New Zealand. In Proceedings of the 16th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. 272–282.
Dani Gunawan, CA Sembiring, and Mohammad Andri Budiman. 2018. The implementation of cosine similarity to calculate text relevance between two documents. In Journal of physics: conference series, Vol. 978. IOP Publishing, 012120.
Andrea Herrmann. 2013. Requirements Engineering in Practice: There Is No Requirements Engineer Position. In Requirements Engineering: Foundation for Software Quality. Springer, 347–361.
Wenxing Hong, Siting Zheng, Huan Wang, and Jianchao Shi. 2013. A job recommender system based on user clustering. J. Comput. 8, 8 (2013), 1960–1967.
Scott A Hurrell, Dora Scholarios, and Paul Thompson. 2013. More than a ‘humpty dumpty’term: Strengthening the conceptualization of soft skills. Economic and Industrial Democracy 34, 1 (2013), 161–182.
Mohamad Kassab, Phillip Laplante, Joanna Defranco, Valdemar Vicente Graciano Neto, and Giuseppe Destefanis. 2021. Exploring the profiles of software testing jobs in the United States. IEEE Access 9 (2021), 68905–68916.
Manoj Kumar, DK Yadav, Ankur Singh, and Vijay Kr Gupta. 2015. A movie recommender system: Movrec. International journal of computer applications 124, 3 (2015).
Suja Cherukullapurath Mana and T Sasipraba. 2021. Research on cosine similarity and pearson correlation based recommendation models. In Journal of Physics: Conference Series, Vol. 1770. IOP Publishing, 012014.
Miriam L Matteson, Lorien Anderson, and Cynthia Boyden. 2016. "Soft skills": A phrase in search of meaning. portal: Libraries and the Academy 16, 1 (2016), 71–88.
Gerardo Matturro. 2013. Soft skills in software engineering: A study of its demand by software companies in Uruguay. In 2013 6th international workshop on cooperative and human aspects of software engineering (CHASE). IEEE, 133–136.
Gerardo Matturro, Florencia Raschetti, and Carina Fontán. 2019. A Systematic Mapping Study on Soft Skills in Software Engineering. J. Univers. Comput. Sci. 25, 1 (2019), 16–41.
Glenford J. Myers, Corey Sandler, and Tom Badgett. 2011. The Art of Software Testing. Wiley, 240 pages.
Frank Nielsen and Frank Nielsen. 2016. Hierarchical clustering. Introduction to HPC with MPI for Data Science (2016), 195–211.
Martin Orsted. 2000. Software development engineer in Microsoft: a subjective view of soft skills required. In Proceedings of the 22nd international conference on Software engineering. 539–540.
Elizabeth Rainsbury, David Leslie Hodges, Noel Burchell, and Mark C Lay. 2002. Ranking workplace competencies: Student and graduate perceptions. (2002).
Luciano Ramalho. 2015. Fluent Python: Clear, concise, and effective programming. " O’Reilly Media, Inc.".
Nikhilesh Sarangpure, Vipul Dhamde, Ankita Roge, Janhawi Doye, Shivam Patle, and Sukhad Tamboli. 2023. Automating the machine learning process using PyCaret and Streamlit. In 2023 2nd International Conference for Innovation in Technology (INOCON). IEEE, 1–5.
Mr Sridhar Dilip Sondur, Mr Amit P Chigadani, and Shantharam Nayak. 2016. Similarity measures for recommender systems: a comparative study. Journal for Research 2, 3 (2016).
Lionel Stebbing. 1993. Quality assurance: the route to efficiency and competitiveness. (No Title) (1993).
Streamlit. 2019. Streamlit: The FastestWay to Build Data Apps. [link]. Acessado: 2024-06-25.
Chong Wang, Pengwei Cui, Maya Daneva, and Mohamad Kassab. 2018. Understanding What Industry Wants from Requirements Engineers: An Exploration of RE Jobs in Canada. In Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). ACM, 10.
David Woodhouse. 2004. The quality of quality assurance agencies. Quality in Higher Education 10, 2 (2004), 77–87.
Shuo Yang, Mohammed Korayem, Khalifeh AlJadda, Trey Grainger, and Sriraam Natarajan. 2017. Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach. Knowledge-Based Systems 136 (2017), 37–45.
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM computing surveys (CSUR) 52, 1 (2019), 1–38.
