Department of Computer Science

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/16

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Author: search.filters.author.Dr. Ghulam RasoolSubject: search.filters.subject.Computer Science

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    Enhancing Automated Question Tagging: Integrating Large Language Models with Traditional Retrieval Methods
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Bilal Hussian; CIIT/SP24-RCS-016/LHR; Dr. Ghulam Rasool; LHR TP 10038
    When people ask questions on platforms like Stack Overflow or Quora, the system needs to automatically assign relevant tags to help others find and answer those questions quickly. This is called automatic question tagging and is quite essential since it directly influences the efficiency with which the user can receive help. The majority of existing tagging systems just examine the words of the question itself and the tags accessible in the database. They are deprived of expansive real-world information that can guide them in making superior tagging decisions. Large language models such as GPT-4 are very good at context, real-world understanding, and this may be invaluable in this case. The issue is, these models cannot be confident in selecting tags out of a list of predefined tags of a particular site that they may propose tags that do not exist in the system at all. To address this problem, I created EAQTILLM (Enhancing Automated Question Tagging Integrating Large Language Model). My method is in two steps: I will first run a tagging model that will give me a shortlist of possible tags based on the database of the website. I then run the question through a large language model with prompts that are designed to explore the question in more depth and choose the most suitable tags using that shortlist. By doing this we get the advantage of both worlds the ability of the LLM to understand and the restriction of just being able to pick out of valid and existing tags. I applied EAQTILLM (Test) to two real-life datasets, achieving a precision of 0.4952, recall of 0.6667, and accuracy of 0.9842, which demonstrated remarkable performance. The algorithm was always ahead of current state-of-the-art algorithms, demonstrating that the combination of the traditional ones and large language models is a good direction to enhance automatic question tagging in online communiti
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    Software Measurements at Small and Medium Enterprises
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2017) Muhammad Noman; FA14-MSCS-018; LHR TP 6871; Dr. Ghulam Rasool
    Software measurement processes are crucial to recognize, assess, identify and measure software products and resources. However, the successful measurement processes implementation is a challenging task in small and medium enterprises (SMEs). The main goal of this thesis is to propose a measurement framework to assess the significant factors that may influence the successful measurement processes implementation in Software SMEs. A multi-method research technique is used to perform this research. A systematic mapping study is conducted to extract the significant factors, challenges, and limitations that may affect the successful implementation of measurement processes in software organizations. A search string is prepared and, applied to different databases (IEEE, ACM, Springer etc) to identify the relevant studies. In total, 24 primary studies are finalized after applying inclusion and exclusion criteria. All the primary studies are explored comprehensively and, identified success factors are categorized in seven major constructs. A measurement framework is proposed for adaptation of measurement processes in software SMEs based on the findings of the mapping study. A total of seven constructs (success factors) are defined in the proposed framework. The proposed framework is evaluated using an online survey. An online survey is conducted from the software professionals in software organizations of Pakistan. In total, 217 responses were collected and a data set is prepared. Structural equation modeling (SEM) technique is used to evaluate the data set. Statistical tools (AMOS and SPSS) are used for factor analysis and hypothesis testing. The statistical results shows that the proposed framework is acceptable due to satisfactory values of model fit indices and use of reliable items in the path diagram. In hypotheses testing, two of the seven constructs (External Forces, Management Commitment) are found to have a significant effect on successful measurement processes in software organizations. The proposed framework can be extended by adding more factors and it can also be verified in software industries of different countries.
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    Detection of Antipatterns from Service Oriented Architecture
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Fatima Sabir; SP14-PCS-003; LHR TP 5603; Dr. Ghulam Rasool
    Web-services have become a governing technology for Service Oriented Architectures due to reusability of services and their dependence on other services. The evolution in service-based systems demand frequent changes to provide quality of service to customers. It is realized by different authors that evolution in service-based systems may degrade design and quality of service and may generate poor solutions known as antipatterns. The detection of antipatterns from web services is an important research realm and it is continuously getting attention of researchers. There are several techniques and tools presented for detection of antipatterns from object-oriented software applications but only few approaches are presented for detection of antipatterns from SOA. The state of the art antipattern detection approaches presented for detection of antipatterns from SOA are not flexible. We present a flexible approach supplemented with a tool support named as SWAD (Specification of Webservice Antipatterns Detection) to detect antipatterns from different SOAP based applications. Service-based systems, in particular, RESTful APIs, need to meet both service consumers’ and providers’ requirements. Like other software systems, RESTful APIs face continuous maintenance and evolution. Antipatterns may hinder the maintenance and evolution of RESTful APIs, as compared to the good design principles, i.e., design patterns that facilitate maintenance and evolution. Antipatterns may also affect the usability of RESTful APIs. Major market players like Facebook and YouTube are already using REST architecture and their APIs are frequently evolving to meet the end users’ requirements. Although, a number of antipatterns are defined in the literature and researchers performed their automatic detection but the evolution of RESTful APIs did not receive much attention. There is a need to track the evolution of antipatterns in the RESTful APIs that could assist service providers publishing well-designed and easy to consume RESTful APIs for their clients. We present the correction of eight REST antipatterns in RESTful APIs with a tool support called SOCAR (Service Oriented Correction of Antipatterns in REST) after analyzing their evolution history for two years. Our correction heuristics are validated by practitioners with an average precision of 100% and an average recall of 94%. Moreover, we propose a methodology for the correction of linguistic antipatterns with a tool support COLAR (Correction of Linguistic Antipatterns for RESTAPIs).