Exploring Technical Debt in Security Questions on Stack Overflow
- URL: http://arxiv.org/abs/2307.11387v1
- Date: Fri, 21 Jul 2023 06:58:01 GMT
- Title: Exploring Technical Debt in Security Questions on Stack Overflow
- Authors: Joshua Aldrich Edbert, Sahrima Jannat Oishwee, Shubhashis Karmakar,
Zadia Codabux, Roberto Verdecchia
- Abstract summary: This study investigates the characteristics of security-related TD questions on Stack Overflow (SO)
We mined 117,233 security-related questions on SO and used a deep-learning approach to identify 45,078 security-related TD questions.
Our analysis revealed that 38% of the security questions on SO are security-related TD questions.
- Score: 3.1041707612049887
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background: Software security is crucial to ensure that the users are
protected from undesirable consequences such as malware attacks which can
result in loss of data and, subsequently, financial loss. Technical Debt (TD)
is a metaphor incurred by suboptimal decisions resulting in long-term
consequences such as increased defects and vulnerabilities if not managed.
Although previous studies have studied the relationship between security and
TD, examining their intersection in developers' discussion on Stack Overflow
(SO) is still unexplored. Aims: This study investigates the characteristics of
security-related TD questions on SO. More specifically, we explore the
prevalence of TD in security-related queries, identify the security tags most
prone to TD, and investigate which user groups are more aware of TD. Method: We
mined 117,233 security-related questions on SO and used a deep-learning
approach to identify 45,078 security-related TD questions. Subsequently, we
conducted quantitative and qualitative analyses of the collected
security-related TD questions, including sentiment analysis. Results: Our
analysis revealed that 38% of the security questions on SO are security-related
TD questions. The most recurrent tags among the security-related TD questions
emerged as "security" and "encryption." The latter typically have a neutral
sentiment, are lengthier, and are posed by users with higher reputation scores.
Conclusions: Our findings reveal that developers implicitly discuss TD,
suggesting developers have a potential knowledge gap regarding the TD metaphor
in the security domain. Moreover, we identified the most common security topics
mentioned in TD-related posts, providing valuable insights for developers and
researchers to assist developers in prioritizing security concerns in order to
minimize TD and enhance software security.
Related papers
- From Data Behavior to Code Analysis: A Multimodal Study on Security and Privacy Challenges in Blockchain-Based DApp [1.6081378516701994]
The recent proliferation of blockchain-based decentralized applications (DApp) has catalyzed transformative advancements in distributed systems.
This study initiates with a systematic analysis of behavioral patterns derived from empirical DApp datasets.
The principal security vulnerabilities in vulnerability-based smart contracts developed via Solidity are then critically examined.
arXiv Detail & Related papers (2025-04-16T08:30:43Z) - A Systematic Review of Security Communication Strategies: Guidelines and Open Challenges [47.205801464292485]
We identify user difficulties including information overload, technical comprehension, and balancing security awareness with comfort.
Our findings reveal consistent communication paradoxes: users require technical details for credibility yet struggle with jargon and need risk awareness without experiencing anxiety.
This work contributes to more effective security communication practices that enable users to recognize and respond to cybersecurity threats appropriately.
arXiv Detail & Related papers (2025-04-02T20:18:38Z) - Towards Trustworthy GUI Agents: A Survey [64.6445117343499]
This survey examines the trustworthiness of GUI agents in five critical dimensions.
We identify major challenges such as vulnerability to adversarial attacks, cascading failure modes in sequential decision-making.
As GUI agents become more widespread, establishing robust safety standards and responsible development practices is essential.
arXiv Detail & Related papers (2025-03-30T13:26:00Z) - Multimodal Situational Safety [73.63981779844916]
We present the first evaluation and analysis of a novel safety challenge termed Multimodal Situational Safety.
For an MLLM to respond safely, whether through language or action, it often needs to assess the safety implications of a language query within its corresponding visual context.
We develop the Multimodal Situational Safety benchmark (MSSBench) to assess the situational safety performance of current MLLMs.
arXiv Detail & Related papers (2024-10-08T16:16:07Z) - From Chaos to Consistency: The Role of CSAF in Streamlining Security Advisories [4.850201420807801]
The Common Security Advisory Format (CSAF) aims to bring security advisories into a standardized format.
Our results show that CSAF is currently rarely used.
One of the main reasons is that systems are not yet designed for automation.
arXiv Detail & Related papers (2024-08-27T10:22:59Z) - A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality [1.7624347338410744]
ChatGPT is a Large Language Model (LLM) that can perform a variety of tasks with remarkable semantic understanding and accuracy.
This study aims to gain an understanding of the potential of ChatGPT as an emerging technology for supporting software security.
It was determined that security practitioners view ChatGPT as beneficial for various software security tasks, including vulnerability detection, information retrieval, and penetration testing.
arXiv Detail & Related papers (2024-08-01T10:14:05Z) - Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? [59.96471873997733]
We propose an empirical foundation for developing more meaningful safety metrics and define AI safety in a machine learning research context.
We aim to provide a more rigorous framework for AI safety research, advancing the science of safety evaluations and clarifying the path towards measurable progress.
arXiv Detail & Related papers (2024-07-31T17:59:24Z) - What Can Self-Admitted Technical Debt Tell Us About Security? A
Mixed-Methods Study [6.286506087629511]
Self-Admitted Technical Debt (SATD)
can be deemed as dreadful sources of information on potentially exploitable vulnerabilities and security flaws.
This work investigates the security implications of SATD from a technical and developer-centred perspective.
arXiv Detail & Related papers (2024-01-23T13:48:49Z) - Communicating on Security within Software Development Issue Tracking [0.0]
We analyse interfaces from prominent issue trackers to see how they support security communication and how they integrate security scoring.
Users in our study were not comfortable with CVSS analysis, though were able to reason in a manner compatible with CVSS.
This suggests that adding improvements to communication through CVSS-like questioning in issue tracking software can elicit better security interactions.
arXiv Detail & Related papers (2023-08-25T16:38:27Z) - Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply
Privacy- and Authenticity-Enhancing Technologies [67.34625604583208]
Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) are considered to engage in Sovereign Data Exchange (SDE)
PETs and AETs are technically complex, which impedes their adoption.
This study empirically constructs a challenge-oriented technology mapping.
arXiv Detail & Related papers (2022-06-20T08:16:42Z) - Unsupervised Person Re-Identification: A Systematic Survey of Challenges
and Solutions [64.68497473454816]
Unsupervised person Re-ID has drawn increasing attention for its potential to address the scalability issue in person Re-ID.
Unsupervised person Re-ID is challenging primarily due to lacking identity labels to supervise person feature learning.
This survey review recent works on unsupervised person Re-ID from the perspective of challenges and solutions.
arXiv Detail & Related papers (2021-09-01T00:01:35Z) - Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance.
We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems.
We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
arXiv Detail & Related papers (2020-10-19T13:09:31Z) - Survey of Network Intrusion Detection Methods from the Perspective of
the Knowledge Discovery in Databases Process [63.75363908696257]
We review the methods that have been applied to network data with the purpose of developing an intrusion detector.
We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods.
As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
arXiv Detail & Related papers (2020-01-27T11:21:05Z) - REST: A Thread Embedding Approach for Identifying and Classifying
User-specified Information in Security Forums [7.222147076297714]
We focus on identifying threads of interest to a security professional.
We propose REST, a systematic methodology to: (a) identify threads of interest based on a, possibly incomplete, bag of words, and (b) classify them into one of the four classes above.
We evaluate our method with real data from three security forums with a total of 164k posts and 21K threads.
arXiv Detail & Related papers (2020-01-08T18:04:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.