Intelligent Automation in Software EngineeRing

Team Lead Research Members Projects

 Mission Statement
Despite 40+ years of extensive research, software maintenance and evolution have never been easy. Studies show that software maintenance claims up to 60% of the total budget of software. Developers also spend at least 50% of their time tackling various maintenance challenges (e.g., software bug resolution, new feature addition). With the rise of popular but highly complex computational frameworks (e.g., Deep Learning, Cloud Computing, Mobile Computing), software maintenance could be even more difficult and costlier. RAISE lab focuses on (a) the better understanding of software maintenance challenges (e.g., software bug resolution, new feature implementation) and (b) designing intelligent, automated, and cost-effective software solutions to overcome these challenges and thus to make the developers' lives easier. RAISE Lab has three signature areas as follows.

Signature Research Areas

Automated software debugging

Software bugs 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 ubiquitous, new classes of bugs are emerging due to the adoption of popular but highly complex technologies such as machine learning, deep learning, cloud computing and Big data analytics. Many of these bugs are hidden not only in the software code but also in other software artifacts such as configuration files, training data, trained models, and deployment modules. Many of them are also non-deterministic and data-driven whereas the bugs are commonly logic-driven. Thus, the traditional debugging techniques that had been developed for the last 40 years might not be enough to tackle these emergent, complex bugs. In the RAISE lab, we study the bugs from various complex software systems including machine learning software. We attempt to (a) better understand the challenges of software debugging with a particular focus on bug/fault localization and bug reproduction that take up ~90% of the debugging time, and (b) design intelligent, explainable, cost-effective software solutions to overcome these challenges.

Automated code search

While finding and fixing bugs in the software is crucial, the majority of the maintenance budget (e.g., 60%) is spent incorporating new features in the existing software. Once a software product is released, requests for new features from the users are very common. To stay competitive in the market, the developers must add cool new features to their software on a regular interval. However, modifying an existing software is not always straight forward. A developer must know (a) how to implement a new feature and (b) where to add this feature within the software code. Thus, as a part of feature addition, developers often search for reusable code snippets not only in the local codebase but also in the web. Unfortunately, they otfen fail to make the right search queries, and 73%--88% of their valuable development time is spent in the trials and errors during code search. In RAISE Lab, we develop state-of-the-art, intelligent tools and techniques that can (a) automatically construct appropriate search queries and (b) find the desired code not only from the local codebase but from the Internet-scale code repositories (e.g., GitHub, SourceForge).

Automated code review

Once software code is modified as a part of bug resolution or feature enhancement, the modified code must be checked for errors or quality issues. Code review has been a vital quality-control practice for the last 30+ years where software code is manually checked by code reviewers (i.e., expert developers) for hidden errors or quality issues. Finding the right reviewers and thus ensuring the high quality of code reviews is a challenging task. Large software companies typically have large, geographically distributed development teams, which contain a tremendous pool of expertise but are too big and diversified for manual assignment of review tasks. Incorrect selection of code reviewers not only delays the reviewing process but also leads to poor reviews. Poorly written reviews often contain inaccurate suggestions and suffer from the lack of clarity or empathy when pointing out mistakes in the software code, which makes the reviews ineffective. As a result, poor-quality reviews become a waste of valuable development effort. Even at Microsoft Corporation, where 50,000+ professional developers spent six hours every week reviewing each other’s code, ~35% of their code reviews are ineffective in improving software quality. Thus, modern code review needs appropriate tools that can make it more productive and more efficient. In RAISE Lab, we design intelligent tools and techniques (a) to find the right code reviewers, and (b) to help the reviewers improve their poor-quality code reviews.

Active Research Topics

  • Bug Localization
  • Bug Reproduction
  • Bug Explanation
  • Duplicate Bug Detection
  • Concept Location
  • Internet-scale Code Search
  • Automated Query Reformulation
  • Automated Question-Answering
  • Code Review Automation
  • Crowdsourced Knowledge Mining
  • Code Comprehension
  • Recommendation Systems in Software Engineering
  • IDE-based Meta Search Engine
  • Exception Handling
  • Software Re-documentation

Research Methodologies

  • Information retrieval
  • Machine learning & Deep learning
  • Explainable AI
  • Genetic Algorithms
  • Mining software repositories
  • Static code analysis
  • Natural language processing
  • Big Data analytics
  • Grounded Theory
  • Empirical studies


Masud Rahman
Assistant Professor (Team Lead)
Faculty of Computer Science
Dalhousie University, Canada.
Research Interets: Please click here!
Bangladesh (cc flaticon.com) Canada (cc flaticon.com)

Parvez Mahbub
MCS (Fall 2021 -- )
Research Interests: Bug understanding, neural text generation, program analysis, and graph neural network.
Bangladesh (cc flaticon.com)
Ohiduzzaman Shuvo
MCS (Fall 2021 -- )
Research Interests: Code review, sentiment analysis, information retrieval, bug localization, and evolutionary algorithms.
Bangladesh (cc flaticon.com)
Sigma Jahan
PhD (Winter 2022 -- )
Research Interests: Bug report management, duplicate bug detection, transfer learning, and deep learning.
Bangladesh (cc flaticon.com)
Usmi Mukherjee
MCS (Winter 2022 -- )
Research Interests: Question-answering, natural language processing, information retrieval, and machine learning.
India (cc flaticon.com)
Asif Samir
PhD (Fall 2022 -- )
Research Interests: Bug report management, question-answering, natural language processing, and information retrieval.
Bangladesh (cc flaticon.com)
Mehil Shah
PhD (Winter 2023 -- )
Research Interests: Bug reproduction, bug localization, machine learning, and deep learning.
India (cc flaticon.com)
Shihui Gao
BCS (Spring/Summer 2022 -- )
Research Interests: Code search and query reformulation.
China (cc flaticon.com)

Message to Prospective Students!

RAISE Lab has been looking for multiple highly motivated, hard working graduate students (one MCS (Canadian/PR) + one PhD) with strong academic background (e.g., CGPA: 3.7/4.00), strong communication/writing skills, and an evidence of research excellence. If you are enthusiastic about solving real-life Software Engineering problems and your credentials meet these requirements, please send your (1) CV (including major achievements), (2) BSc/MSc transcripts, (3) IELTS/TOEFL scores (more details), (4) sample publications, and (5) one-page summary of your past research excellence to masud[DOT]rahman[AT]dal[DOT]ca. The one-page summary should also include how your past experience might align with our research goals and interests. It should be noted that only potentially eligible candidates might be contacted for an interview due to a high volume of applications. More details about the admission into Dalhousie University.

Co-supervised/Mentored Students at University of Saskatchewan
  • Saikat Mondal (PhD), Geddes Award 2019, Best MSc Thesis Award 2020, U of S Graduate Thesis Award 2020, GSA STEM Award 2022
  • Rodrigo Fernandes (PhD), CROKAGE

Co-supervised/Mentored Students at Polytechnique Montreal
  • Biruk Asmare Muse (PhD)
  • Mehdi Moravati (PhD)
  • Mahmood Vahedi (MSc)
  • Hadhemi Jebnoun (MSc)

Completed Projects (All Publications)

Explaining Software Bugs Leveraging Code Structures in Neural Machine Translation (ICSE 2023)
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.

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

Towards Understanding the Impacts of Textual Dissimilarity on Duplicate Bug Report Detection (SANER 2023)
Overview: A large-scale empirical study using 92K bug reports from three open-source systems is done to understand the challenges of textual dissimilarity in duplicate bug report detection. First, empirical evidence is demonstrated using existing techniques that poorly detect textually dissimilar duplicate bug reports. Second, we found that textually dissimilar duplicates often miss essential components (e.g., steps to reproduce), which could lead to their textual dissimilarity within the same pair. Finally, inspired by the earlier findings, domain-specific embedding along with CNN is applied to duplicate bug report detection, which provides mixed results. Explore more ...

information-retrieval deep-learning duplicate-bug-detection empirical-study

The Forgotten Role of Search Queries in IR-based Bug Localization: An Empirical Study [EMSE 2021, ICSE 2022 (Journal First)]
Overview: We conduct an in-depth empirical study that critically examines the state-of-the-art query selection practices in IR-based bug localization. In particular, we use a dataset of 2,320 bug reports, employ ten existing approaches from the literature, exploit the Genetic Algorithm-based approach to construct optimal, near-optimal search queries from these bug reports, and then answer three research questions. We confirmed that the state-of-the-art query construction approaches are indeed not sufficient for constructing appropriate queries (for bug localization) from certain natural language-only bug reports although they contain such queries. We also demonstrate that optimal queries and non-optimal queries chosen from bug report texts are significantly different in terms of several keyword characteristics, which has led us to actionable insights. Furthermore, we demonstrate 27%--34% improvement in the performance of non-optimal queries through the application of our actionable insights to them. Explore more ...

query-reformulation bug-localization empirical-study  genetic-algorithm

A Systematic Literature Review of Automated Query Reformulations in Source Code Search (TOSEM/Under Revision)
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 six 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

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

BugDoctor: Intelligent Search Engine for Software Bugs and Features [ICSE-C 2019]
Overview: Bug Doctor assists the developers in localizing the software code of interest (e.g., bugs, concepts and reusable code) during software maintenance. In particular, it reformulates a given search query (1) by designing a novel keyword selection algorithm (e.g., CodeRank) that outperforms the traditional alternatives (e.g., TF-IDF), (2) by leveraging the bug report quality paradigm and source document structures which were previously overlooked and (3) by exploiting the crowd knowledge and word semantics derived from Stack Overflow Q&A site, which were previously untapped. An experiment using 5000+ search queries (bug reports, change requests, and ad hoc queries) suggests that Bug Doctor can improve the given queries significantly through automated query reformulations. Comparison with 10+ existing studies on bug localization, concept location and Internet-scale code search suggests that Bug Doctor can outperform the state-of-the-art approaches with a significant margin. Explore more ...

query-reformulation bug-localization concept-location code-search

BLIZZARD: Improving IR-Based Bug Localization with Context-Aware Query Reformulation [ESEC/FSE 2018]
Overview: BLIZZARD is a novel technique for IR-based bug localization that uses query reformulation and bug report quality dynamics. We first conduct an empirical study to analyse the report quality dynamics of bug reports and then design an IR-based bug localization technique using graph-based keyword selection, query reformulation, noise filtration, and Information Retrieval. Explore more ...

query-reformulation   bug-localization

STRICT: Search Term Identification for Concept Location using Graph-Based Term Weighting [SANER 2017]
Overview: STRICT is a novel technique for identifying appropriate search terms from a software change request. It uses graph-based term weighting (PageRank), natural language processing and Information Retrieval to identify the important keywords and then finds the code of interest (e.g., software feature). Explore more ...

query-reformulation  concept-location

ACER: Improved Query Reformulation for Concept Location using CodeRank and Document Structures [ASE 2017]
Overview: ACER offers effective reformulations to queries for concept location using CodeRank and source document structures. It uses graph-based keyword selection from source code, query difficulty analysis, machine learning and Information Retrieval for reformulating the queries. Explore more ...

query-reformulation concept-location

RACK: Automatic Query Reformulation for Code Search using Crowdsourced Knowledge [SANER 2016 + EMSE 2019 + ICSE 2017]
Overview: We propose a novel query reformulation technique--RACK--that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers of Stack Overflow (i.e., crowdsourced knowledge). We first motivate our idea using an exploratory study with 19 standard Java API packages and 344K Java related posts from Stack Overflow. Experiments using 175 code search queries randomly chosen from three Java tutorial sites show that our technique recommends correct API classes within the Top-10 results for 83% of the queries, with 46% mean average precision and 54% recall, which are 66%, 79% and 87% higher respectively than that of the state-of-the-art. Reformulations using our suggested API classes improve 64% of the natural language queries and their overall accuracy improves by 19%. Comparisons with three state-of-the-art techniques demonstrate that RACK outperforms them in the query reformulation by a statistically significant margin. Investigation using three web/code search engines shows that our technique can significantly improve their results in the context of code search. Explore more ...

query-reformulation code-search stack-overflow

NLP2API: Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics [ICSME 2018]
Overview: NLP2API reformulates a natural language query intended for Internet-scale code search using crowdsourced knowledge and extra-large data analytics derived from Stack Overflow Q & A site. Explore more ...

query-reformulation  code-search stack-overflow

BLADER: Improving Queries for IR-Based Bug Localization with Semantics-Driven Query Reformulation [Ongoing Work]
Overview: BLADER complements a bug report that does not contain any structured entities (e.g., program entity names) and uses the complemented report as a query for IR-based bug localization. It employs word embeddings derived from Stack Overflow with FastText for the query reformulation. Explore more ...

query-reformulation   bug-localization

CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project and Technology Experience [ICSE 2016 + ASE 2016]
Overview: We propose a heuristic ranking technique that considers not only the cross-project work history of a developer but also her experience in certain technologies associated with a pull request for determining her expertise as a potential code reviewer. We first motivate our technique using an exploratory study with 20 commercial projects. We then evaluate the technique using 13,081 pull requests from ten projects, and report 92.15% accuracy, 85.93% precision and 81.39% recall in code reviewer recommendation which outperforms the state-of-the-art technique. This project was funded by NSERC Industry Engage grant and was done in collaboration with Vendasta Technologies. Explore more ...

code-review-automation data-mining

RevHelper: Predicting Usefulness of Code Review Comments using Textual Features and Developer Experience [MSR 2017]
Overview: RevHelper predicts the usefulness of code review comments based on their texts and developers' experience. We first conduct an empirical study where we contrast between hundreds of useful and non-useful review comments from multiple commercial projects. Then we collect features from comment texts and reviewers' experience, and apply Random Forest algorithm to them for the usefulness prediction. Explore more ...

code-review-automation data-mining

CodeInsight: Recommending Insightful Comments for Source Code using Crowdsourced Knowledge [SCAM 2015]
Overview: CodeInsight is an automated technique for generating insightful comments for source code using crowdsourced knowledge from Stack Overflow. It uses data mining, topic modelling, sentiment analysis and heuristics for deriving the code-level insights. Explore more ...

data-mining stack-overflow

ExcClipse: Context-Aware Meta Search Engine for Programming Errors and Exceptions
Overview: In this MSc thesis, we develop a context-aware, IDE-based, meta search engine --ExcClipse-- that delivers relevant web pages and code examples within the IDE panel for dealing with programming errors and exceptions. Once a programming error/exception is encountered, the tool (1) constructs an appropriate query by capturing the error details and meta data, (2) collects results from popular search engines--Google, Bing, Yahoo, StackOverflow and GitHub, (3) refines and ranks the results against the context of the encountered exception, and (4) then recommends them within the IDE. We develop our solution as an Eclipse plug-in prototype. Explore more ...

recommendation-system search-engine stack-overflow

SurfClipse: Context-Aware IDE-Based Meta Search Engine for Programming Errors & Exceptions [CSMR-WCRE 2014 + ICSME 2014 + WCRE 2013]
Overview: We propose a context-aware meta search tool, SurfClipse, that analyzes an encountered exception andits context in the IDE, and recommends not only suitable search queries but also relevant web pages for the exception (and its context). The tool collects results from three popular search engines and a programming Q & A site against the exception in the IDE, refines the results for relevance against the context of the exception, and then ranks them before recommendation. It provides two working modes--interactive and proactive to meet the versatile needs of the developers, and one can browse the result pages using a customized embedded browser provided by the tool. Explore more ...

recommendation-system search-engine stack-overflow

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Copyright © Mohammad Masudur Rahman. Last updated on January 02, 2023