Najam Nazar

Najam Nazar, Christoph Treude

Background: Automated code summarisation supports program comprehension and documentation, yet the relative strengths and limitations of deterministic (heuristic-based) and probabilistic (LLM-based) pipelines remain unclear. Aims: This paper presents a controlled empirical comparison of these paradigms for intent-oriented design-pattern code summarisation. Method: Using design-pattern-centric Java code as a structured testbed (150 files from three open-source repositories covering nine patterns), we compare a rule-based natural language generation (NLG) pipeline, a Software Word Usage Model (SWUM)-based approach, and a probabilistic pipeline based on the Mixtral LLM. Summaries are evaluated against human references using BERTScore and cosine similarity, complemented by rubric-based judgements produced by Llama 3 across five dimensions: accuracy, conciseness, adequacy, code-context awareness, and design-pattern fidelity. Statistical analysis includes Wilcoxon signed-rank tests (with effect sizes), Friedman tests with post-hoc corrections, and Spearman correlation for sensitivity analysis of rubric consistency. Results: Probabilistic summaries show stronger semantic alignment and richer contextual coverage, while deterministic approaches produce more concise and fully reproducible outputs. Prompt-sensitivity and multi-run analyses indicate variability in LLM outputs, though relative trends remain stable. Conclusions: A clear trade-off emerges: probabilistic methods favour semantic depth and contextual accuracy, whereas deterministic pipelines are preferable for brevity and reproducibility. These findings provide practical guidance for selecting code summarisation techniques.

Najam Nazar

Software Product Line Engineering enables systematic reuse across families of related software intensive systems. This survey synthesises key SPLE foundations, lifecycle concepts, adoption models, tooling and AI era challenges. Based on a structured review of the SPLE literature, we compare major adoption and evaluation models, including BAPO, FEF, PuLSE, SIMPLE, COPLIMO, PROMOTE-PL, and APPLIES. We further summarise the historical evolution of SPLE research from domain engineering foundations to AI assisted variability management. The survey also examines tool interoperability, UVL-based standardisation, SME adoption, migration from clone-and-own development, variability aware DevOps, empirical evidence gaps and assurance challenges for AI assisted SPLE. The paper provides a compact research agenda for software engineering and ICT researchers by consolidating open challenges and future research directions in contemporary SPLE.

Najam Nazar, Norman Chen, Chun Yong Chong

While constructing supervised learning models, we require labelled examples to build a corpus and train a machine learning model. However, most studies have built the labelled dataset manually, which in many occasions is a daunting task. To mitigate this problem, we have built an online tool called CodeLabeller. CodeLabeller is a web-based tool that aims to provide an efficient approach to handling the process of labelling source code files for supervised learning methods at scale by improving the data collection process throughout. CodeLabeller is tested by constructing a corpus of over a thousand source files obtained from a large collection of open source Java projects and labelling each Java source file with their respective design patterns and summaries. Twenty five experts in the field of software engineering participated in a usability evaluation of the tool using the standard User Experience Questionnaire online survey. The survey results demonstrate that the tool achieves the Good standard on hedonic and pragmatic quality standards, is easy to use and meets the needs of the annotating the corpus for supervised classifiers. Apart from assisting researchers in crowdsourcing a labelled dataset, the tool has practical applicability in software engineering education and assists in building expert ratings for software artefacts.

Najam Nazar, Aldeida Aleti, Yaokun Zheng

Software design patterns are standard solutions to common problems in software design and architecture. Knowing that a particular module implements a design pattern is a shortcut to design comprehension. Manually detecting design patterns is a time consuming and challenging task, therefore, researchers have proposed automatic design pattern detection techniques. However, these techniques show low performance for certain design patterns. In this work, we introduce a design pattern detection approach, DPD_F that improves the performance over the state-of-the-art by using code features with machine learning classifiers to automatically train a design pattern detector. DPD_F creates a semantic representation of Java source code using the code features and the call graph, and applies the \textit{Word2Vec} algorithm on the semantic representation to construct the word-space geometric model of the Java source code. DPD$_F$ then builds a Machine Learning classifier trained on a labelled dataset and identifies software design patterns with over 80% Precision and over 79\% Recall. Additionally, we have compared DPD_F with two existing design pattern detection techniques namely FeatureMaps & MARPLE-DPD. Empirical results demonstrate that our approach outperforms the state-of-the-art approaches by approximately 35% and 15% respectively in terms of Precision. The run-time performance also supports the practical applicability of our classifier.