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 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 software budget.
Developers 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., Large Language Models, Deep learning, Cloud Computing, Mobile Computing), software maintenance has become even more difficult and costlier.
RAISE lab focuses on (a) a better understanding of software maintenance and evolution 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 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 Deep learning, Cloud computing, and Simulation models. Many of these bugs are hidden not only in the software code but also in other artifacts such as configuration files, training data, trained models, deployment modules, and even simulation models. Many of them are also non-deterministic and data-driven whereas the traditional software bugs are commonly logic-driven. Thus, the traditional debugging techniques that have been developed for the last 40+ years might not be enough to tackle these emerging, complex bugs. In the RAISE lab, we study the bugs from various complex software systems including machine learning software and simulation modelling software. We attempt to (a) better understand the challenges of software debugging with a particular focus on bug/fault localization, bug understanding, 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 correcting 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 new, attractive features to their software at regular intervals. However, modifying an existing software 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, 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 often fail to make the right search queries. According to existing surveys, they might even fail up to 73%-88% of the time, which is a significant waste of their valuable development time. With the growing interest in prompt engineering in the context of large language models, search queries have been never more important. 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 ensuring the high quality of code reviews are 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 a 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 spend 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 to (a) automatically find the right code reviewers, and (b) automatically improve poor-quality code reviews.  Active Research Topics
RAISE Team
RAISE Graduates
RAISE Interns & Visitors
Join RAISE!
No funded position right now! However, please book an appointment if you want to discuss your research interests. 
Recent Projects
Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical
Study [EMSE 2024]
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 challenge. 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 
Explaining Software Bugs Leveraging Code Structures in Neural Machine
Translation [ICSE 2023 + ICSME 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. 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]
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
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
Recommending Code Reviews Leveraging Code Changes with Structured Information
Retrieval [ICSME 2023]
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
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
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
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All Projects & Publications
Automated Software Debugging|
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
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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
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
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
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
Automated Code Review
Recommending Code Reviews Leveraging Code Changes with Structured
Information Retrieval [ICSME 2023]
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
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
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CORRECT: Code Reviewer Recommendation in GitHub
Based on Cross-Project and Technology Experience [ICSE
2016 + ASE 2016]
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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
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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
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
Automated Code Search
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 leverages the co-occurrences and syntactic dependencies among terms as a proxy to their importance. STRICT uses graph-based term weighting (PageRank), natural language processing and Information Retrieval to identify the important keywords from a change request, and then finds the code of interest (e.g., software feature). Explore more ...
query-reformulation concept-location
Overview: STRICT is a novel technique for identifying appropriate search terms from a software change request. It leverages the co-occurrences and syntactic dependencies among terms as a proxy to their importance. STRICT uses graph-based term weighting (PageRank), natural language processing and Information Retrieval to identify the important keywords from a change request, 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 search queries leveraging CodeRank, an adaptation of PageRank to source code documents. It uses graph-based term weighting, query difficulty analysis, machine learning, and Information Retrieval to reformulate queries and find the code of interest. Explore more ...
query-reformulation concept-location
Overview: ACER offers effective reformulations to search queries leveraging CodeRank, an adaptation of PageRank to source code documents. It uses graph-based term weighting, query difficulty analysis, machine learning, and Information Retrieval to reformulate queries and find the code of interest. Explore more ...
query-reformulation concept-location
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RACK: Automatic Query Reformulation for Code Search using
Crowdsourced Knowledge [SANER 2016 + EMSE 2019 + ICSE
2017]
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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
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NLP2API: Effective Reformulation of Query for Code Search using
Crowdsourced Knowledge and Extra-Large Data Analytics [ICSME 2018]
Overview: NLP2API expands a natural language query, intended for Internet-scale code search, leveraging crowdsourced knowledge and extra-large data analytics (e.g., semantic similarity) derived from Stack Overflow Q & A site. It also leverages Borda count to rank the most appropriate API classes for a given query. Explore more ...
query-reformulation code-search stack-overflow
Overview: NLP2API expands a natural language query, intended for Internet-scale code search, leveraging crowdsourced knowledge and extra-large data analytics (e.g., semantic similarity) derived from Stack Overflow Q & A site. It also leverages Borda count to rank the most appropriate API classes for a given query. Explore more ...
query-reformulation code-search stack-overflow
CROKAGE: Effective Solution Recommendations for Programming Tasks by
Leveraging Crowd Knowledge [ICPC 2019 + EMSE 2020 + JSS 2021]
Overview: In this work, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) as input and delivers a comprehensible solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations written by human developers. Explore more ...
query-reformulation search-engine stack-overflow
Overview: In this work, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) as input and delivers a comprehensible solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations written by human developers. Explore more ...
query-reformulation search-engine stack-overflow
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SurfClipse: Context-Aware IDE-Based Meta Search
Engine for Programming Errors & Exceptions [CSMR-WCRE
2014 + ICSME 2014 + WCRE 2013]
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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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ExcClipse: Context-Aware Meta Search Engine for Programming Errors and
Exceptions
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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
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Software
Quality Control
On the Prevalence, Evolution, and Impact of Code Smells in Simulation Modelling Software
[SCAM 2024]
Overview: In this work, we detect code smells in simulation modelling systems and contrast their prevalence, evolution, and impact with those of traditional software systems. We found that code smells are more prevalent and long-lived in simulation systems. Explore more ...
simulation-system code-smells
Overview: In this work, we detect code smells in simulation modelling systems and contrast their prevalence, evolution, and impact with those of traditional software systems. We found that code smells are more prevalent and long-lived in simulation systems. Explore more ...
simulation-system code-smells
The Scent of Deep Learning Code: An Empirical Study [MSR 2020]
Overview: In this work, we perform a comparative analysis between deep learning and traditional open-source applications collected from GitHub. We have several major findings. First, long lambda expression, long ternary conditional expression, and complex container comprehension smells are frequently found in deep learning projects. That is, deep learning code involves more complex or longer expressions than the traditional code does. Second, the number of code smells increases across the releases of deep learning applications. Third, we found that there is a co-existence between code smells and software bugs in the studied deep learning code, which confirms our conjecture on the degraded code quality of deep learning applications. Explore more ...
deep-learning code-smells
Overview: In this work, we perform a comparative analysis between deep learning and traditional open-source applications collected from GitHub. We have several major findings. First, long lambda expression, long ternary conditional expression, and complex container comprehension smells are frequently found in deep learning projects. That is, deep learning code involves more complex or longer expressions than the traditional code does. Second, the number of code smells increases across the releases of deep learning applications. Third, we found that there is a co-existence between code smells and software bugs in the studied deep learning code, which confirms our conjecture on the degraded code quality of deep learning applications. Explore more ...
deep-learning code-smells
On the Prevalence, Impact, and Evolution of SQLcode smells in Data-Intensive Systems [MSR
2020]
Overview: In this work, we conduct an empirical study to investigate the prevalence and evolution of SQL code smells in open-source, data-intensive systems. We collected 150 projects and examined both traditional and SQL code smells in these projects. Overall, our results show that SQL code smells are indeed prevalent and persistent in the studied data-intensive software systems. Developers should be aware of these smells and consider detecting and refactoring SQL code smells and traditional code smells separately, using dedicated tools. Explore more ...
deep-learning code-smells
Overview: In this work, we conduct an empirical study to investigate the prevalence and evolution of SQL code smells in open-source, data-intensive systems. We collected 150 projects and examined both traditional and SQL code smells in these projects. Overall, our results show that SQL code smells are indeed prevalent and persistent in the studied data-intensive software systems. Developers should be aware of these smells and consider detecting and refactoring SQL code smells and traditional code smells separately, using dedicated tools. Explore more ...
deep-learning code-smells
Automated Q&A
The Reproducibility of Programming-Related Issues in Stack Overflow Questions [MSR 2019 +
EMSE 2021]
Overview: In this work, we conducted an exploratory study on the reproducibility of issues discussed in 400 Java and 400 Python questions. We parsed, compiled, executed, and carefully examined the code segments from these questions to reproduce the reported programming issues. The study found that approximately 68% of Java and 71% of Python issues were reproducible, with many requiring modifications, while 22% of Java and 19% of Python issues were irreproducible. It also revealed that reproducible questions are twice as likely to receive accepted answers more quickly, with confounding factors like reputation not affecting this correlation. Explore more ...
stack-overflow question-answering
Overview: In this work, we conducted an exploratory study on the reproducibility of issues discussed in 400 Java and 400 Python questions. We parsed, compiled, executed, and carefully examined the code segments from these questions to reproduce the reported programming issues. The study found that approximately 68% of Java and 71% of Python issues were reproducible, with many requiring modifications, while 22% of Java and 19% of Python issues were irreproducible. It also revealed that reproducible questions are twice as likely to receive accepted answers more quickly, with confounding factors like reputation not affecting this correlation. Explore more ...
stack-overflow question-answering
Can We Identify Stack Overflow Questions Requiring Code Snippets? Investigating the Cause &
Effect of Missing Code Snippets [SANER 2024]
Overview: In this study, we conduct an empirical study investigating the cause & effect of missing code snippets in SO questions whenever required. The study shows that questions including required code snippets during submission are three times more likely to receive accepted answers, and confounding factors like user reputation do not affect this correlation. A survey of practitioners revealed that 60% of users are unaware of when code snippets are needed in their questions. To address this, the researchers developed machine learning models with high accuracy (85.2%) to predict questions needing code snippets, potentially improving programming Q&A efficiency and the quality of the knowledge base. Explore more ...
stack-overflow question-answering
Overview: In this study, we conduct an empirical study investigating the cause & effect of missing code snippets in SO questions whenever required. The study shows that questions including required code snippets during submission are three times more likely to receive accepted answers, and confounding factors like user reputation do not affect this correlation. A survey of practitioners revealed that 60% of users are unaware of when code snippets are needed in their questions. To address this, the researchers developed machine learning models with high accuracy (85.2%) to predict questions needing code snippets, potentially improving programming Q&A efficiency and the quality of the knowledge base. Explore more ...
stack-overflow question-answering
Do Subjectivity and Objectivity Always Agree? A Case Study with Stack Overflow Questions
[MSR 2023]
Overview: In this article, we compare the subjective assessment of questions with their objective assessment using 2.5 million questions and ten text analysis metrics. According to our investigation, (1) four objective metrics agree, (2) two metrics do not agree, (3) one metric either agrees or disagrees, and (4) the remaining three metrics neither agree nor disagree with the subjective evaluation. We then develop machine learning models to classify the promoted and discouraged questions. Our models outperform the state-of-the-art models with a maximum of about 76%--87% accuracy. Explore more ...
stack-overflow question-answering
Overview: In this article, we compare the subjective assessment of questions with their objective assessment using 2.5 million questions and ten text analysis metrics. According to our investigation, (1) four objective metrics agree, (2) two metrics do not agree, (3) one metric either agrees or disagrees, and (4) the remaining three metrics neither agree nor disagree with the subjective evaluation. We then develop machine learning models to classify the promoted and discouraged questions. Our models outperform the state-of-the-art models with a maximum of about 76%--87% accuracy. Explore more ...
stack-overflow question-answering
An Insight into the Unresolved Questions at Stack Overflow [MSR 2015]
Overview: In this paper, we investigate 3,956 such unresolved questions using an exploratory study where we analyze four important aspects of those questions, their answers and the corresponding users that partially explain the observed scenario. We then propose a prediction model by employing five metrics related to user behaviour, topics and popularity of question, which predicts if the best answer for a question at Stack Overflow might remain unaccepted or not. Experiments using 8,057 questions show that the model can predict unresolved questions with 78.70% precision and 76.10% recall. Explore more ...
stack-overflow question-answering
Overview: In this paper, we investigate 3,956 such unresolved questions using an exploratory study where we analyze four important aspects of those questions, their answers and the corresponding users that partially explain the observed scenario. We then propose a prediction model by employing five metrics related to user behaviour, topics and popularity of question, which predicts if the best answer for a question at Stack Overflow might remain unaccepted or not. Experiments using 8,057 questions show that the model can predict unresolved questions with 78.70% precision and 76.10% recall. Explore more ...
stack-overflow question-answering
Theses & Dissertations
Parvez Mahbub (MCS). Comprehending Software Bugs Leveraging Code Structures with Neural
Language Models (Summer 2023)
Overview: This thesis introduces Bugsplorer, a deep-learning technique for line-level defect prediction, demonstrating 26-72% better accuracy than existing methods and efficient ranking of vulnerable lines. Additionally, Bugsplainer, a transformer-based generative model, provides natural language explanations for software bugs, outperforming multiple baselines according to evaluation metrics and a developer study with 20 participants. The empirical evidence suggests that these techniques have the potential to substantially reduce Software Quality Assurance costs. Explore more ...
bug-explanation deep-learning neural-text-generation transformer-based-model
Overview: This thesis introduces Bugsplorer, a deep-learning technique for line-level defect prediction, demonstrating 26-72% better accuracy than existing methods and efficient ranking of vulnerable lines. Additionally, Bugsplainer, a transformer-based generative model, provides natural language explanations for software bugs, outperforming multiple baselines according to evaluation metrics and a developer study with 20 participants. The empirical evidence suggests that these techniques have the potential to substantially reduce Software Quality Assurance costs. Explore more ...
bug-explanation deep-learning neural-text-generation transformer-based-model
Ohiduzzaman Shuvo (MCS). Improving Modern Code Review Leveraging Contextual and Structural
Information from Source Code (Summer 2023)
Overview: This thesis first conducts an empirical study, revealing significant performance variations in existing techniques for assessing code reviews between open-source and closed-source systems. The study indicates that less experienced developers tend to submit more non-useful review comments in both contexts, emphasizing the need for automated support in code review composition. To help developers write better review comments, another technique namely RevCom was proposed. RevCom utilizes structured information retrieval and outperforms both Information Retrieval (IR)-based and Deep Learning (DL)-based baselines, offering a lightweight and scalable solution with the potential to alleviate cognitive effort and save time for reviewers. Explore more ...
structured-information-retrieval code-review-automation
Overview: This thesis first conducts an empirical study, revealing significant performance variations in existing techniques for assessing code reviews between open-source and closed-source systems. The study indicates that less experienced developers tend to submit more non-useful review comments in both contexts, emphasizing the need for automated support in code review composition. To help developers write better review comments, another technique namely RevCom was proposed. RevCom utilizes structured information retrieval and outperforms both Information Retrieval (IR)-based and Deep Learning (DL)-based baselines, offering a lightweight and scalable solution with the potential to alleviate cognitive effort and save time for reviewers. Explore more ...
structured-information-retrieval code-review-automation
Usmi Mukherjee (MCS). Complementing Deficient Bug Reports with Missing Information Leveraging
Neural Text Generation (Fall 2023)
Overview: This thesis introduces and assesses two novel Generative AI approaches for enhancing deficient bug reports. The first approach, BugMentor, utilizes structured information retrieval and neural text generation to provide contextually appropriate answers to follow-up questions in bug reports, demonstrating superior performance over three existing baselines in terms of metrics such as BLEU and Semantic Similarity. A developer study further validates BugMentor's effectiveness in generating more accurate, precise, concise, and useful answers. The second approach, BugEnricher, fine-tunes a T5 model on software-specific vocabulary to generate meaningful explanations, outperforming two baselines and showing promise in improving the detection of textually dissimilar duplicate bug reports, a known challenge in bug report management. The empirical evidence suggests these approaches hold strong potential for supporting bug resolution and enhancing bug report management. Explore more ...
bug-report-enhancement deep-learning neural-text-generation transformer-based-model
Overview: This thesis introduces and assesses two novel Generative AI approaches for enhancing deficient bug reports. The first approach, BugMentor, utilizes structured information retrieval and neural text generation to provide contextually appropriate answers to follow-up questions in bug reports, demonstrating superior performance over three existing baselines in terms of metrics such as BLEU and Semantic Similarity. A developer study further validates BugMentor's effectiveness in generating more accurate, precise, concise, and useful answers. The second approach, BugEnricher, fine-tunes a T5 model on software-specific vocabulary to generate meaningful explanations, outperforming two baselines and showing promise in improving the detection of textually dissimilar duplicate bug reports, a known challenge in bug report management. The empirical evidence suggests these approaches hold strong potential for supporting bug resolution and enhancing bug report management. Explore more ...
bug-report-enhancement deep-learning neural-text-generation transformer-based-model
Lareina Yang (BCS). Search Term Identification for Concept Location Leveraging Word Relations
(Winter 2024)
Overview: The thesis extends an existing approach called STRICT to improve keyword selection for software change requests using graph-based algorithms like TextRank, POSRank, SimRank, Biased TextRank, and PositionRank. Experiments show that the enhanced approach, STRICT++, outperforms STRICT in detecting software bugs, with significant performance improvements in metrics like MAR, MRR, and Top-10 Accuracy. The thesis emphasizes the importance of capturing syntactic relationships and considers factors like word similarity, task-specific biases, and position information for effective keyword extraction. Explore more ...
bug-localization concept-location query-reformulation information-retrieval 
Overview: The thesis extends an existing approach called STRICT to improve keyword selection for software change requests using graph-based algorithms like TextRank, POSRank, SimRank, Biased TextRank, and PositionRank. Experiments show that the enhanced approach, STRICT++, outperforms STRICT in detecting software bugs, with significant performance improvements in metrics like MAR, MRR, and Top-10 Accuracy. The thesis emphasizes the importance of capturing syntactic relationships and considers factors like word similarity, task-specific biases, and position information for effective keyword extraction. Explore more ...
bug-localization concept-location query-reformulation information-retrieval
Callum MacNeil (BCS). A Systematic Review of Automated Program Repair using Large Language
Models (Fall 2023)
Overview: This thesis conducts a systematic review of automated program repair tools leveraging large language models, scrutinizing 1,276 papers published between 2017 and 2023 and narrowing down the analysis to 53 primary studies. The findings indicate a prevalent choice among these studies to utilize popular datasets and pre-trained models, specifically Defects4j and codeT5. The review reveals a tendency for studies to target specific aspects of program repair, such as validation or input representation, and underscores challenges in input representation, evaluation methods, and learning objectives, emphasizing the pivotal trade-off between efficacy and efficiency. Leveraging this comprehensive understanding, the thesis identifies future directions and best approaches for research on automated program repair using large language models. Explore more ...
bug-fixing deep-learning neural-code-generation transformer-based-model
Overview: This thesis conducts a systematic review of automated program repair tools leveraging large language models, scrutinizing 1,276 papers published between 2017 and 2023 and narrowing down the analysis to 53 primary studies. The findings indicate a prevalent choice among these studies to utilize popular datasets and pre-trained models, specifically Defects4j and codeT5. The review reveals a tendency for studies to target specific aspects of program repair, such as validation or input representation, and underscores challenges in input representation, evaluation methods, and learning objectives, emphasizing the pivotal trade-off between efficacy and efficiency. Leveraging this comprehensive understanding, the thesis identifies future directions and best approaches for research on automated program repair using large language models. Explore more ...
bug-fixing deep-learning neural-code-generation transformer-based-model
Shihui Gao (BCS). Code Search in the IDE with Query Reformulation (Fall 2023)
Overview: The thesis enhances the existing code search solution, RACK, by reimplementing it in Python and addressing the code search problem more effectively. The author constructs a token-API database through the analysis of Python posts on Stack Overflow, employing three co-occurrence-based heuristics—KKC, KAC, and KPAC—to establish relevance between natural language queries and API classes. The integrated RACK implementation becomes a VS-code plugin, enabling developers to input natural language queries and swiftly retrieve pertinent code examples from GitHub, thereby accelerating the problem-solving process in programming. Explore more ...
query-reformulation code-search stack-overflow 
Overview: The thesis enhances the existing code search solution, RACK, by reimplementing it in Python and addressing the code search problem more effectively. The author constructs a token-API database through the analysis of Python posts on Stack Overflow, employing three co-occurrence-based heuristics—KKC, KAC, and KPAC—to establish relevance between natural language queries and API classes. The integrated RACK implementation becomes a VS-code plugin, enabling developers to input natural language queries and swiftly retrieve pertinent code examples from GitHub, thereby accelerating the problem-solving process in programming. Explore more ...
query-reformulation code-search stack-overflow
Disclaimer: The overview of each thesis has been generated from their original abstracts using ChatGPT.
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