Once a software product is released, it enters into the maintenance phase and goes through many major or minor changes. These changes (a.k.a., maintenance activities) can be triggered by several factors including post-release bugs, requests for new features, or even by the need to adapt to new technologies (e.g., Generative AI). Unfortunately, these activities have never been easy despite 40+ years of extensive research! Software maintenance activities claim up to ~50% of the developers' time and cost the global economy billions of dollars every year. For example, in 2017 alone, the global economy lost $1.7+ trillion due to software bugs, crashes, and failures. With the rise of popular but highly complex computing frameworks such as Large Language Models (LLM), Agentic AI, Deep Learning, Cloud Computing, and Mobile Computing, software maintenance has become even more challenging and costlier.

Signature Research Areas

Automation of Software Debugging

Software bugs, crashes, and failures are inevitable! In 2017 alone, 606 software bugs cost the global economy $1.7 trillion+, with 3.7 billion people affected and 314 companies impacted! While these bugs are hard to tackle, new classes of software bugs have emerged due to the adoption of popular but highly complex technologies such as Large Language Models (LLM), Agentic AI, Deep learning, Cloud computing, and Mobile Computing. Many of these bugs are hidden not only in the software code but also in other artifacts including configuration files, training data, model parameters, and deployment modules. They are also non-deterministic, multi-faceted, and data-driven unlike the traditional software bugs, which are commonly logic-driven. Thus, the traditional methods of debugging, developed over the last 40+ years, might not be enough to tackle these emerging, complex bugs.

In RAISE Lab, we aim to (a) better understand the challenges of software bugs, crashes, vulnerabilities, and technical debt, and (b) design cost-effective, cutting-edge, AI-powered solutions to tackle them effectively. In particular, we (a) study the characteristics, symptoms, and root causes of software bugs to advance the current state of understanding and (b) develop novel tools and techniques to automatically detect, diagnose, explain, and reproduce bugs or crashes from not only traditional but also AI-powered and simulation modelling software. In essence, our research opportunistically marries Artificial Intelligence (AI) and Software Engineering (SE) to tackle a challenge (a.k.a., software bug) plaguing the software industry for decades.

AI for Code Review

Once software code is modified, it must be checked for errors or quality issues. Over the last 35+ years, software developers have been using code review, an engineering practice, to ensure the quality of the software code. However, it is often a time-consuming and error-prone process, which can be further exacerbated by the increasing complexity of modern software systems and the selection of inappropriate reviewers. Poorly written reviews often contain inaccurate suggestions and suffer from a lack of clarity or empathy when pointing out mistakes in the software code, which makes the reviews ineffective. Even at Microsoft Corporation, where 50,000+ professional developers spend six hours every week reviewing each other’s code, ~35% of their code reviews are ineffective in improving software quality.

In RAISE Lab, we design novel, intelligent tools and techniques to (a) find the right code reviewers, (b) generate effective code reviews, and (c) improve poor-quality code reviews. In particular, we leverage a blend of traditional (e.g., Information Retrieval, Mining Software Repositories) and AI-powered solutions (e.g., LLM) to develop our code review solutions.

AI for Code Search

While resolving bugs in the software is crucial, the majority of the maintenance budget (e.g., ~50%) is spent incorporating new features in the existing software (a.k.a., feature enhancement). However, modifying an existing software containing thousands of lins of code is not always straightforward. A developer must know (a) how to implement a new feature and (b) where to add this feature within the software code. Thus, developers often search for the right location within the the codebase by manually formulating and executing queries from a feature request. However, according to existing surveys, they might fail to find the right location ~88% of the time, which is a significant waste of their valuable development time.

In RAISE Lab, we develop state-of-the-art, intelligent tools and techniques for code search by capitalizing on the strengths of traditional methods (e.g., Information Retrieval, query reformulation) and AI-powered solutions (e.g., Large Language Models, Prompt Optimization, Agentic AI).

In RAISE Lab, we have been advancing the highlighted steps (e.g., light blue, light orange) of the software maintenance and evolution pipelines below.

Research Topics, Methods & Technologies

Active Research Topics Bug Localization Bug Reproduction Bug Explanation Defect Prediction Duplicate Bug Detection Bug Report Enrichment Deep Learning Bugs Code Review Generation Question-Answering Code Search & Query Reformulation Research Methods & Technologies Information Retrieval (IR) Program Analysis Mining Software Repositories Large Language Models (LLM) Agentic AI Explainable AI Deep Learning Machine Learning Natural Language Processing Empirical Studies Grounded Theory Other Research Interests Crowdsourced Knowledge Mining Code Comprehension Recommendation Systems in SE IDE-based Meta Search Engine Exception Handling Software Re-documentation

RAISE Team

	Masud Rahman Associate Professor (Team Lead) Faculty of Computer Science Dalhousie University, Canada. Research Interets: Please click here!
Sigma Jahan PhD (Winter 2022 -- ) Research Interests: Bug localization, deep learning, transfer learning, and duplicate bug report detection. Usmi Mukherjee PhD (Winter 2024 -- ) Research Interests: Question-answering, information retrieval, generative AI models, and simulation models. Co-supervised By: Dr. Ahsan Habib, Dalhousie University. Asif Samir PhD (Fall 2022 -- ) Research Interests: Bug localization, question-answering, generative AI models, and information retrieval. Mehil Shah PhD (Winter 2023 -- ) Research Interests: Bug reproduction, bug localization, machine learning, and deep learning. Co-supervised By: Dr. Foutse Khomh, Polytechnique Montreal. Wendi Wanyan BCS (Spring 2025 -- ) Research Interests: Bug explanation, Crash Reports, Data mining, Machine Learning, and Generative AI.

RAISE Graduates

Parvez Mahbub MCS (Summer 2023) Thesis: Comprehending Software Bugs Leveraging Code Structures with Neural Language Models. Current Position: Software Engineer, Siemens Canada, Saskatoon Ohiduzzaman Shuvo MCS (Summer 2023) Thesis: Improving Modern Code Review Leveraging Contextual and Structural Information from Source Code. Current Position: IT Application System Analyst, City of Red Deer and Instructor, CIOT, Calgary Usmi Mukherjee MCS (Fall 2023) Thesis: Complementing Deficient Bug Reports with Missing Information Leveraging Neural Text Generation. Current Position: PhD Student, Dalhousie University. Riasat Mahbub MCS (Summer 2025) Thesis: Understanding Code Smells and Refactoring Practices in Simulation Modelling Systems - A Comprehensive Study. Co-supervised By: Dr. Ahsan Habib, Dalhousie University. Shihui Gao BCS (Fall 2023) Thesis: Code Search in the IDE with Query Reformulation. Current Position: Data Analyst, EMC, Halifax. Lareina Yang BCS (Winter 2023 -- ) Thesis: Search Term Identification for Concept Location Leveraging Word Relations. Current Position: MS Student, Cornell University, New York. Callum MacNeil BCS (Fall 2023) Thesis: A Systematic Review of Automated Program Repair using Large Language Models. Current Position: MCS Student, Dalhousie University.

RAISE Interns & Visitors

Md Nizamuddin BCS (Spring/Summer 2024) Research Interests: Domain term classification, and duplicate bug detection. Current Position: BCS Student, SRM Institute of Science and Technology. Abdul Samad BCS (Winter 2025) Research Interests: Software debugging, Query difficulty analysis, and Generative AI. Current Position: BCS Student, Dalhousie University. Jitansh Arora BCS (Fall 2024) Research Interests: Code review automation and Generative AI. Current Position: BCS Student, Dalhousie University.

Join RAISE!

Recent Projects

Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification [ICSE 2025]

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Overview: Deep Neural Networks (DNNs) have been widely used in various applications, such as autonomous vehicles, medical diagnosis, software development, and natural language processing. However, DNNs are prone to faults, which can lead to severe consequences. In this work, we present DEFault (Detect and Explain Fault) -- a novel technique to detect and diagnose faults in DNN programs. It first captures dynamic (i.e., runtime) features during model training and leverages a hierarchical classification approach to detect all major fault categories from the literature. Then, it captures static features (e.g., layer types) from DNN programs and leverages explainable AI methods (e.g., SHAP) to narrow down the root cause of the fault. We train and evaluate DEFault on a large, diverse dataset of ~14.5K DNN programs and further validate our technique using a benchmark dataset of 52 real-life faulty DNN programs. Our approach achieves ~94% recall in detecting real-world faulty DNN programs and ~63% recall in diagnosing the root causes of the faults, demonstrating 3.92%--11.54% higher performance than that of state-of-the-art techniques. Explore more ...


deep-learning bug-localization  tools-techniques 

Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study [EMSE 2024]

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Overview: Deep learning has seen significant progress across various fields. However, like any software system, deep learning models have bugs, some with serious consequences, such as crashes in autonomous vehicles. Reproducing deep learning bugs, a prerequisite for their resolution, has been a major challeng According to an existing work, only 3% of deep learning bugs are reproducible. In this work, we investigate deep learning bug reproducibility by creating a dataset of 668 bugs from platforms like Stack Overflow and GitHub and by reproducing 165 bugs. While reproducing these bugs, we identify edit actions and useful information for their reproduction. Then we used the Apriori algorithm to identify useful information and edit actions required to reproduce specific types of bugs. In a study involving 22 developers, our recommended information from the Apriori algorithm increased bug reproducibility by 22.92% and reduced reproduction time by 24.35%. These findings provide valuable insights to help practitioners and researchers enhance reproducibility in deep learning systems. Explore more ...


deep-learning empirical-study bug-reproduction

Improved IR-based Bug Localization with Intelligent Relevance Feedback [ICPC 2025]

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Overview: In this project, we present a novel technique - BRaIn - for bug localization that addresses the contextual gaps between bug reports and source code using Large Language Models (LLM). We leverage LLM’s feedback (a.k.a., Intelligent Relevance Feedback) to reformulate queries and re-rank source documents, improving bug localization. We evaluate BRaIn using a benchmark dataset --Bench4BL-- and three performance metrics and compare it against six baseline techniques from the literature. Our experimental results show that BRaIn outperforms baselines by 87.6%, 89.5%, and 48.8% margins in MAP, MRR, and HIT@K, respectively. Additionally, it can localize ~52% of bugs that cannot be localized by the baseline techniques due to the poor quality of corresponding bug reports. Explore more ...


query-reformulation bug-localization  Large-Language-Model  information-retrieval tools-techniques 

Explaining Software Bugs Leveraging Code Structures in Neural Machine Translation [ICSE 2023 + ICSME 2023]

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Overview: Software bugs claim ≈ 50% of development time and cost the global economy billions of dollars. Over the last five decades, there has been significant research on automatically finding or correcting software bugs. However, there has been little research on automatically explaining the bugs to the developers, which is a crucial but highly challenging task. To fill this gap, we developed Bugsplainer, a transformer-based generative model, that generates natural language explanations for software bugs by learning from a large corpus of bug-fix commits. Bugsplainer can leverage structural information and buggy patterns from the source code to generate an explanation for a bug. A developer study involving 20 participants shows that the explanations from Bugsplainer are more accurate, more precise, more concise and more useful than the baselines. Explore more ...


bug-explanation deep-learning neural-text-generation transformer-based-model

A Systematic Review of Automated Query Reformulations in Source Code Search [TOSEM 2023]

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Overview: In this systematic literature review, we carefully select 70 primary studies on query reformulations from 2,970 candidate studies, perform an in-depth qualitative analysis using the Grounded Theory approach, and then answer seven important research questions. Our investigation has reported several major findings. First, to date, eight major methodologies (e.g., term weighting, query-term co-occurrence analysis, thesaurus lookup) have been adopted in query reformulation. Second, the existing studies suffer from several major limitations (e.g., lack of generalizability, vocabulary mismatch problem, weak evaluation, the extra burden on the developers) that might prevent their wide adoption. Finally, we discuss several open issues in search query reformulations and suggest multiple future research opportunities. Explore more ...


query-reformulation bug-localization concept-location code-search empirical-study  grounded-theory

Recommending Code Reviews Leveraging Code Changes with Structured Information Retrieval [ICSME 2023]

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Overview: Review comments are one of the main building blocks of modern code reviews. Manually writing code review comments could be time-consuming and technically challenging. In this work, we propose a novel technique for relevant review comments recommendation -- RevCom -- that leverages various code-level changes using structured information retrieval. It uses different structured items from source code and can recommend relevant reviews for all types of changes (e.g., method-level and non-method-level). We find that RevCom can recommend review comments with an average BLEU score of ≈ 26.63%. According to Google's AutoML Translation documentation, such a BLEU score indicates that the review comments can capture the original intent of the reviewers. Our approach is lightweight compared to DL-based techniques and can recommend reviews for both method-level and non-method-level changes where the existing IR-based technique falls short. Explore more ...


structured-information-retrieval code-review-automation

Why Are Some Bugs Non-Reproducible? An Empirical Investigation using Data Fusion [ICSME 2020 + EMSE 2022]

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Overview: We conduct a multimodal study to better understand the non-reproducibility of software bugs. First, we perform an empirical study using 576 non-reproducible bug reports from two popular software systems (Firefox, Eclipse) and identify 11 key factors that might lead a reported bug to non-reproducibility. Second, we conduct a user study involving 13 professional developers where we investigate how the developers cope with non-reproducible bugs. We found that they either close these bugs or solicit for further information, which involves long deliberations and counter-productive manual searches. Third, we offer several actionable insights on how to avoid non-reproducibility (e.g., false-positive bug report detector) and improve reproducibility of the reported bugs (e.g., sandbox for bug reproduction) by combining our analyses from multiple studies (e.g., empirical study, developer study). Explore more ...


empirical-study   data-fusion   bug-reproduction  grounded-theory  

All Projects & Publications

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