Comparison of Machine Learning Methods: Conclusions and Future Work, and References

Table of Links

• Abstract and Introduction
• Background
• Approach
• Experiments and Results
• Discussion and Qualitative Analysis
• Related Work
• Conclusions and Future Work, and References

7 Conclusions and Future Work

This study focuses on automated issue assignment using proprietary issue reports obtained from the electronic product manufacturer’s issue tracking system. The objective of the issue assignment approach is to assign issues to appropriate team members based on their respective fields. The team members are categorized into Software Developer, Software Tester, Team Leader, and UI/UX Designer. Among these categories, the majority of the data set consists of developers. Efficiently allocating issues to developers is critical for effective time management. To achieve this, we further classify developers into Senior, Mid, and Junior levels, which are widely accepted labels in the industry.

Our focus lies in extracting features from the filled Jira columns, as well as the title and description texts of the issues, utilizing NLP techniques. These features serve as inputs to our learning methods, enabling us to analyze and classify the issues effectively. Additionally, we employ other commonly used word embedding methods which are Tf-Idf, BOW, and Word2Vec to generate feature vectors from the text fields. This step, implemented using the Sklearn and Gensim library, allows us to compare the performance of our feature set against alternative approaches. Furthermore, to assess the effectiveness of our overall methodology, we incorporate widely adopted deep-learning techniques, namely DistilBert, Roberta, and Electra.

Following the production of feature vectors, we proceed to implement the proposed system utilizing established machine learning techniques. With the aim of enhancing predictive performance, we employ ensemble methods that leverage a diverse range of machine-learning algorithms. To evaluate the effectiveness of our system, we employ widely recognized metrics such as accuracy, precision, recall, and F1-score which serve as indicators of its performance. To further refine our predictions, we employ a robust technique known as 10-fold crossvalidation. In order to conduct a thorough statistical analysis, we construct a matrix to compare and contrast the effectiveness of our proposed strategies. This matrix allows us to assess the performance of our system across different algorithms, ensemble techniques, and evaluation metrics.

Our future endeavors involve the development of a versatile tool applicable to diverse software team models. To fortify our work, we actively engage in discussions and pursue collaborations to acquire data sets from businesses operating across various domains, such as game development and banking applications. This broadened data set will enable us to enhance our model’s capabilities for multi-class classification, accommodating different roles within software teams, including product owners and business analysts. Furthermore, we are committed to ensuring compatibility and flexibility by incorporating various business branches into our data set. By incorporating real-world data obtained directly from industry sources, both in English and Turkish, we will conduct comprehensive evaluations through diverse studies. Expanding on the existing features, we intend to utilize the same data set for future research endeavors, such as effort estimation [53,60], further solidifying the value and applicability of our work in the field.

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