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. 

Exploring Technical Debt in Security Questions on Stack Overflow

Technical Debt in Microservices: A Mixed-Method Case Study

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.

pdf/exploring-technical-debt-in-security-questions-on-stack-5dpqrqq0cs.pdf

In today’s fast-paced software industry, understanding and managing Technical Debt (TD) is crucial for software development. TD can compromise the long-term quality of software systems. The occurrence of TD is commonly reported and discussed by practitioners on Question and Answers (Q&A) platforms, such as Stack Overflow (SO). Data from Q&A platforms has been leveraged by the TD research community, most prominently regarding knowledge extraction. However, manual analyses of such data not only require considerable effort but also suffer from biases. Hence, this paper aims to propose an automated approach for identifying and classifying types of TD in SO discussions using machine learning (ML) and natural language processing. We divided our methodology into four main steps: i) data preprocessing, ii) application of natural language processing, iii) application of ML algorithms, and iv) computing the evaluation metrics for the proposed models. Our results indicate that ML algorithms have the potential to be successfully applied to automatically identify and classify TD types on SO discussions.We achieved a recall of 85% for test debt and a precision of 78% for design debt. Furthermore, the results of automated TD identification on SO benefit the software development community by enhancing solution quality, raising awareness of best practices, and facilitating collaboration among developers. This leads to more efficient development and the promotion of consistent standards. We make our entire dataset and pre-trained models available to encourage future research directions.

Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions

. The recent surge in sentiment analysis within software engineering texts has gained prominence due to its proven influence on productivity and collaborative work quality. Stack Overflow, a substantial repository of software engineering content, has become a focal point for sentiment analysis research, particularly in software development. Nevertheless, integrating sentiment analysis into software engineering has unveiled potential shortcomings in their efficacy. Researchers have had to adapt these tools to navigate limitations, as exemplified by the cases of SentiCR and Senti4SD. Moreover, natural language processing has made significant strides in recent times, primarily attributed to the emergence of pre-trained transformer-based models. Addressing this research gap, the study introduces a dedicated sentiment analysis model, BertSentiment-SO. This study delves explicitly into sentiment analysis for the three foremost cloud service providers: Amazon, Google, and Microsoft. Extracted from the Big Query public dataset, the data undergoes preprocessing and labelling via a semi-supervised learning approach. Harnessing the capabilities of BERT, our proposed model outperforms its counterparts, enabling a more precise score of 94.06% for the evaluation of sentiment within the domain of cloud services.

BertSentiment-SO Model: Extracting Cloud Service Information from Stack Overflow’s Technical Texts

[Context and motivation] Twitter is one of the most widely used micro-blogging platforms. Globally distributed developers and software companies use Twitter to communicate about software updates, bugs and other type of information related to the software. End-users from diverse geographical regions also use Twitter to give feedback about the software they use. Previous research has shown that this feedback is valuable for requirements engineering, containing information such as feature requests and usage scenarios. However, the effect of the country of origin on software-related tweets has not been studied so far. [Question] In this paper, we investigate to what extent people from various countries provide distinct feedback regarding certain characteristics on Twitter. [Principal ideas/results] We collected 70,759 tweets (Original: 17,940, Replies: 52,819) from popular Twitter support accounts of ten software applications for two months. In the subsequent analysis, we selected the tweets originating from the eight most popular countries and analyzed a sample of 1,813 tweets with the help of automatic and manual content analysis. Results show that out of three characteristics (content, sentiment and text length); content, and sentiment differ significantly at the country level in some cases. These characteristics are used in algorithms automatically processing user feedback. Such algorithms are commonly used for requirements engineering tasks. [Contributions] Our findings show the importance of considering software-related user feedback on Twitter from a diverse audience during the design, testing, and validation of feedback processing algorithms to minimize bias concerning different countries of origin. 

Towards a Cross-Country Analysis of Software-Related Tweets

From airports to banks, healthcare, space crafts, and even amazon services, technology impacts almost every aspect of today’s life. If wrongdoings occur within or in relation to technology, they can have big implications on individuals, groups of people, or society as a whole. Whistleblowers are insiders who expose such wrongdoings— eventually stopping misconducts, such as fraud, endangerment to public health and safety, or damage to the environment. Twitter is a microblogging service that allows millions of users to share their views with people distributed all over the world on a daily basis. Tweets have the potential to contain useful information about whistleblowing in tech, from the general public and whistleblowers. However, until now this point has not been researched.To fill this gap, we conducted an exploratory study on technology-related whistleblowing tweets by manually analysing tweets, utilising descriptive statistics, and machine learning techniques. We mined 7,400 tweets from whistleblowers themselves, as well as news and opinions about certain whistleblowers and whistleblowing cases. Although our results show that only 30% of the tweets in our sample dataset (obtained through specific search terms) contained relevant information about whistleblowing in technology, our analysis shows that tweets provide valuable information for both researchers and companies to understand the public opinion regarding whistleblowing cases. Furthermore, we found that machine learning techniques are promising means for extracting information about whistleblowing in tech from the vast stream of tweets.

Whistleblowing and Tech on Twitter

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Background: Q&A sites allow to study how users reference and request support on technical debt. To date only few studies, focusing on narrow aspects, investigate technical debt on Stack Overflow. Aims: We aim at gaining an in-depth understanding on the characteristics of technical debt questions on Stack Overflow. In addition, we assess if identification strategies based on machine learning can be used to automatically identify and classify technical debt questions. Method: We use automated and manual processes to identify technical debt questions on Stack Overflow. The final set of 415 questions is analyzed to study (i) technical debt types, (ii) question length, (iii) perceived urgency, (iv) sentiment, and (v) themes. Natural language processing and machine learning techniques are used to assess if questions can be identified and classified automatically. Results: Architecture debt is the most recurring debt type, followed by code and design debt. Most questions display mild urgency, with frequency of higher urgency steadily declining as urgency rises. Question length varies across debt types. Sentiment is mostly neutral. 29 recurrent themes emerge. Machine learning can be used to identify technical debt questions and binary urgency, but not debt types. Conclusions: Different patterns emerge from the analysis of technical debt questions on Stack Overflow. The results provide further insights on the phenomenon, and support the adoption of a more comprehensive strategy to identify technical debt questions.

/pdf/asking-about-technical-debt-characteristics-and-automatic-2eif62yp.pdf

Asking about Technical Debt: Characteristics and Automatic Identification of Technical Debt Questions on Stack Overflow

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Towards a Cross-Country Analysis of Software-Related Tweets

Whistleblowing and Tech on Twitter

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Asking about Technical Debt: Characteristics and Automatic Identification of Technical Debt Questions on Stack Overflow