Saeed Siddik

Work place: Institute of Information Technology, University of Dhaka, Dhaka, Bangladesh



Research Interests:


Saeed Siddik has been working on Software Testing and Software Analysis research where he experimented how software are developed and tested efficiently. He has completed his M.Sc. in Software Engineering, including the highest marked thesis dissertation on Software Test Case Prioritization from IIT University of Dhaka. The research outcomes of that thesis were published at several Journal and Conferences. He was the first research student of IITDU Optimization Research group, where he was working on software design migration to enhance modularity and manageability. He is a member of IEEE (ID:94159542).

Author Articles
Non-Functional Requirements Classification Using Machine Learning Algorithms

By Abdur Rahman Abu Nayem Saeed Siddik

DOI:, Pub. Date: 8 Jun. 2023

Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functional requirements classification performance. We also reported the best approach for classifying Non-functional requirements. We conducted the comparative analysis on a publicly available PROMISE_exp dataset containing labelled functional and Non-functional requirements. Initially, we normalized the textual requirements from the dataset; then extracted features through Bag of Words (BoW), Term Frequency and Inverse Document Frequency (TF-IDF), Hashing and Chi-Squared vectorization methods. Finally, we executed the 15 most popular ML algorithms to classify the requirements. The novelty of this work is the empirical analysis to find out the best combination of ML classifier with appropriate vectorization technique, which helps developers to detect Non-functional requirements early and take precise steps. We found that the linear support vector classifier and TF-IDF combination outperform any combinations with an F1-score of 81.5%.

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Sentiment Analysis on Twitter Data: Comparative Study on Different Approaches

By Abdur Rahman Mobashir Sadat Saeed Siddik

DOI:, Pub. Date: 8 Aug. 2021

Social media has become incredibly popular these days for communicating with friends and for sharing opinions. According to current statistics, almost 2.22 billion people use social media in 2016, which is roughly one third of the world population and three times of the entire population in Europe. In social media people share their likes, dislikes, opinions, interests, etc. so it is possible to know about a person’s thoughts about a specific topic from the shared data in social media. Since, twitter is one of the most popular social media in the world; it is a very good source for opinion mining and sentiment analysis about different topics. In this research, SVM with different kernel functions and Adaboost are experimented using CPD and Chi-square feature extraction techniques to explore the best sentiment classification model. The reported average accuracy of Adaboost for Chi-square and CPD are 70.2% and 66.9%. The SVM radial basis kernel and polynomial kernel with Chi-square n-grams reported average accuracy of 73.73% and 68.67% respectively. Among the performed experimentation, SVM sigmoid kernel with Chi-square n-grams provided the maximum accuracy that is 74.4%.

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Subset Matching based Selection and Ranking (SMSR) of Web Services

By Md. Abdur Rahman Md. Belal Hossain Md. Sharifur Rahman Saeed Siddik

DOI:, Pub. Date: 8 Apr. 2019

Web service is a software application, which is accessible using platform independent and language neutral web protocols. However, selecting the most relevant services became one of the vital challenges. Quality of services plays very important role in web service selection, as it determines the quality and usability of a service, including its non-functional properties such as scalability, accessibility, integrity, efficiency, etc. When agent application send request with a set of quality attributes, it becomes challenging to find out the best service for satisfying maximum quality requirements. Among the existing approaches, the single value decomposition technique is popular one; however, it suffers for computational complexity. To overcome this limitation, this paper proposed a subset matching based web service selection and ranking by considering the quality of service attributes. This proposed method creates a quality-web matrix to store available web services and associated quality of service attributes. Then, matrix subsets are created using web service repository and requested quality attributes. Finally, web services are efficiently selected and ranked based on calculated weights of corresponding web services to reduce composition time. Experimental results showed that proposed method performs more efficient and scalable than existing several techniques such as single value decomposition.

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