Software practitioners use online forums to navigate complex and often ambiguous legal privacy requirements, yet little is known about their professional backgrounds, what challenges they face, and how they use and assess the credibility of the advice received, or how they resolve ambiguities in posts. We report the findings of a survey of 223 Reddit users from regulatory-focused subreddits, complemented by a qualitative analysis of 2,248 posts and responses. Our results show that, despite holding privacy-related certifications, most participants frequently use forums to seek legal advice. Key challenges reported or identified include implementing a data protection impact assessment, reporting a data breach, and obtaining cookie consent. Reddit users often assess credibility by reviewing respondents' post history, verifying sources cited, trusting advice from recognized experts, and following up for clarity before responding. We highlight research and educational directions to bridge gaps in support needed for regulatory compliance guidance.

Privacy law and regulation have turned to “consent” as the legitimate basis for collecting and processing individuals’ data. As governments have rushed to enshrine consent requirements in their privacy laws, such as the California Consumer Privacy Act (CCPA), significant challenges remain in understanding how these legal mandates are operationalized in software. The opaque nature of software development processes further complicates this translation. To address this, we explore the use of Large Language Models (LLMs) in requirements engineering to bridge the gap between legal requirements and technical implementation. This study employs a three-step pipeline that involves using an LLM to classify software use cases for compliance, generating LLM modifications for non-compliant cases, and manually validating these changes against legal standards. Our preliminary findings highlight the potential of LLMs in automating compliance tasks, while also revealing limitations in their reasoning capabilities. By benchmarking LLMs against real-world use cases, this research provides insights into leveraging AI-driven solutions to enhance legal compliance of software.

In software engineering, requirements may be acquired from stakeholders through elicitation methods, such as interviews, observational studies, and focus groups. When supporting acquisition from interviews, business analysts must review transcripts to identify and document requirements. Goal modeling is a popular technique for representing early stakeholder requirements as it lends itself to various analyses, including refinement to map high-level goals into software operations, and conflict and obstacle analysis. In this paper, we describe an approach to use textual entailment to reliably extract goals from interview transcripts and to construct goal models. The approach has been evaluated on 15 interview transcripts across 29 application domains. The findings show that GPT-4o can reliably extract goals from interview transcripts, matching 62.0% of goals acquired by humans from the same transcripts, and that GPT-4o can trace goals to originating text in the transcript with 98.7% accuracy. In addition, when evaluated by human annotators, GPT-4o generates goal model refinement relationships among extracted goals with 72.2% accuracy.

Interviews are a widely used technique in eliciting requirements to gather stakeholder needs, preferences, and expectations for a software system. Effective interviewing requires skilled interviewers to formulate appropriate interview questions in real time while facing multiple challenges, including lack of familiarity with the domain, excessive cognitive load, and information overload that hinders how humans process stakeholders’ speech. Recently, large language models (LLMs) have exhibited state-of-the-art performance in multiple natural language processing tasks, including text summarization and entailment. To support interviewers, we investigate the application of GPT-4o to generate follow-up interview questions during requirements elicitation by building on a framework of common interviewer mistake types. In addition, we describe methods to generate questions based on interviewee speech. We report a controlled experiment to evaluate LLM-generated and human-authored questions with minimal guidance, and a second controlled experiment to evaluate the LLM-generated questions when generation is guided by interviewer mistake types. Our findings demonstrate that, for both experiments, the LLM-generated questions are no worse than the human-authored questions with respect to clarity, relevancy, and informativeness. In addition, LLM-generated questions outperform human-authored questions when guided by common mistakes types. This highlights the potential of using LLMs to help interviewers improve the quality and ease of requirements elicitation interviews in real time.

Software systems must comply with legal regulations, which is a resource-intensive task, particularly for small organizations and startups lacking dedicated legal expertise. Extracting metadata from regulations to elicit legal requirements for software is a critical step to ensure compliance. However, it is a cumbersome task due to the length and complex nature of legal text. Although prior work has pursued automated methods for extracting structural and semantic metadata from legal text, key limitations remain: they do not consider the interplay and interrelationships among attributes associated with these metadata types, and they rely on manual labeling or heuristic-driven machine learning, which does not generalize well to new documents.In this paper, we introduce an approach based on textual entailment and in-context learning for automatically generating a canonical representation of legal text—encodable and executable as Python code. Our representation is instantiated from a manually designed Python class structure that serves as a domain-specific metamodel, capturing both structural and semantic legal metadata and their interrelationships. This design choice reduces the need for large, manually labeled datasets and enhances applicability to unseen legislation. We evaluate our approach on 13 U.S. state data breach notification laws, demonstrating that our generated representations pass approximately 89.4% of test cases and achieve a precision and recall of 82.2 and 88.7 respectively.

Language models that can learn a task at inference time, called in-context learning (ICL), show increasing promise in natural language inference tasks. In ICL, a model user constructs a prompt to describe a task with a natural language instruction and zero or more examples, called demonstrations. The prompt is then input to the language model to generate a completion. In this paper, we apply ICL to the design and evaluation of satisfaction arguments, which describe how a requirement is satisfied by a system specification and associated domain knowledge. The approach builds on three prompt design patterns, including augmented generation, prompt tuning, and chain-of-thought prompting, and is evaluated on a privacy problem to check whether a mobile app scenario and associated design description satisfies eight consent requirements from the EU General Data Protection Regulation (GDPR). The overall results show that GPT-4 can be used to verify requirements satisfaction with 96.7% accuracy and dissatisfaction with 93.2% accuracy. Inverting the requirement improves verification of dissatisfaction to 97.2%. Chain-of-thought prompting improves overall GPT-3.5 performance by 9.0% accuracy. We discuss the trade-offs among templates, models and prompt strategies and provide a detailed analysis of the generated specifications to inform how the approach can be applied in practice.

Companies use personalization to tailor user experiences. Personalization appears in search engines and online stores, which include salutations and statistically learned correlations over search-, browsing- and purchase-histories. However, users have a wider variety of substantive, domain-specific preferences that affect their choices when they use directory services, and these have largely been overlooked or ignored. The contributions of this paper include: (1) a grounded theory describing how stakeholder preferences are expressed in text scenarios; (2) an app feature survey to assess whether elicited preferences represent missing requirements in existing systems; (3) an evaluation of three classifiers to label preference words in scenarios; and (4) a linker to build preference phrases by linking labeled preference words to each other based on word position. In this study, the authors analyzed 217 elicited directory service scenarios across 12 domain categories to yield a total of 7,661 stakeholder preferences labels. The app survey yielded 43 stakeholder preferences that were missed on average 49.7% by 15 directory service websites studied. The BERT-based transformer showed the best average overall 81.1% precision, 84.4% recall and 82.6% F1-score when tested on unseen domains. Finally, the preference linker correctly links preference phrases with 90.1% accuracy. Given these results, we believe directory service developers can use this approach to automatically identify user preferences to improve service designs.

Mobile applications (apps) provide users valuable benefits at the risk of exposing users to privacy harms. Improving privacy in mobile apps faces several challenges, in particular, that many apps are developed by low resourced software development teams, such as end-user programmers or in startups. In addition, privacy risks are primarily known to users, which can make it difficult for developers to prioritize privacy for sensitive data. In this paper, we introduce a novel, lightweight method that allows app developers to elicit scenarios and privacy risk scores from users directly using only an app screenshot. The technique relies on named entity recognition (NER) to identify information types in user-authored scenarios, which are then fed in real-time to a privacy risk survey that users complete. The best-performing NER model predicts information types with a weighted average precision of 0.70 and recall of 0.72, after post-processing to remove false positives. The model was trained on a labeled 300-scenario corpus, and evaluated in an end-to-end evaluation using an additional 203 scenarios yielding 2,338 user-provided privacy risk scores. Finally, we discuss how developers can use the risk scores to prioritize, select and apply privacy design strategies in the context of four user-authored scenarios.

Advances in personalization of digital services are driven by low-cost data collection and processing, in addition to the wide variety of third-party frameworks for authentication, storage, and marketing. New privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act, increasingly require organizations to explicitly state their data practices in privacy policies. When data practices change, a new version of the policy is released. This can occur a few times a year, when data collection or processing requirements are rapidly changing. Consent evolution raises specific challenges to ensuring GDPR compliance. We propose a formal consent framework to support organizations, data users, and data subjects in their understanding of policy evolution under a consent regime that supports both the retroactive and non-retroactive granting and withdrawal of consent. The contributions include (i) a formal framework to reason about data collection and access under multiple consent granting and revocation scenarios, (ii) a scripting language that implements the consent framework for encoding and executing different scenarios, (iii) five consent evolution use cases that illustrate how organizations would evolve their policies using this framework, and (iv) a scalability evaluation of the reasoning framework. The framework models are used to verify when user consent prevents or detects unauthorized data collection and access. The framework can be integrated into a runtime architecture to monitor policy violations as data practices evolve in real time. The framework was evaluated using the five use cases and a simulation to measure the framework scalability. The simulation results show that the approach is computationally scalable for use in runtime consent monitoring under a standard model of data collection and access and practice and policy evolution.

Mobile apps are widely used and often process users’ sensitive data. Many taint analysis tools have been applied to analyze sensitive information flows and report data leaks in apps. These tools require a list of sources (where sensitive data is accessed) as input, and researchers have constructed such lists within the Android platform by identifying Android API methods that allow access to sensitive data. However, app developers may also define methods or use third-party library’s methods for accessing data. It is difficult to collect such source methods, because they are unique to the apps, and there are a large number of third-party libraries available on the market that evolve over time. To address this problem, we propose DAISY, a Dynamic-Analysis-Induced Source discoverY approach for identifying methods that return sensitive information from apps and third-party libraries. Trained on an automatically labeled dataset of methods and their calling context, DAISY identifies sensitive methods in unseen apps. We evaluated DAISY on real-world apps, and the results show that DAISY can achieve an overall precision of 77.9% when reporting the most confident results. Most of the identified sources and leaks cannot be detected by existing technologies.

Software and hardware innovation has led to new consumer products and services with significant benefits to consumers and society. These advances, however, can come with great cost to society when they fail to comply with government laws and regulations. While compliance failures do result from technical missteps in design, there is also a wide gap between the technical expertise and culture of legal analysts and software engineers, as well as competing priorities between legal requirements and business objectives. In this perspective paper, we propose changing legal compliance from a corporate oversight activity to a principal design activity, wherein lawyers and software engineers employ enhanced methods and tools tailored to bridge the cultural and knowledge gap and assess legal and business trade-offs. To that end, we describe a new software quality, called Legal Accountability, which can be evaluated alongside other qualities, such as usability, modifiability, performance and testing. Legal Accountability has five properties that lawyers and designers must attend to, including legal traceability, completeness, validity, auditability and continuity. We illustrate the quality with examples from the U.S. data processing perspective, and prior work in requirements engineering, before concluding with future and ongoing research challenges.

Security analysts rely on scenarios to assess vulnerabilities, project attacks, and decide on security requirements that mitigate the threat. However, eliciting natural language scenarios from stakeholders can be an ad-hoc process and subject to ambiguity and incompleteness. In this article, we examine systematic scenario elicitation by introducing a method based on user stories that uses a simplified process model of iterative scenario refinement. The process consists of three steps: 1) eliciting an interaction statement that describes a critical action performed by a user or system process; 2) eliciting one or more descriptive statements about a technology that enables the interaction; and 3) refinement of the technology into technical variants that correspond to design alternatives. We empirically evaluated our method by implementing our prototype in a user study that collects 30 security scenarios from participants. Based on our analysis, our proposed method is shown effective. Participants had a 100 percent task completion rate with 57 percent of participants achieving complete task-success, and the remaining 43 percent of participants achieving partial task-success. We also show the effect of security domain knowledge, and the benefit of using structure when collecting security requirements in natural language text. Finally, we present lessons learned and future research directions.

Privacy policies are legal documents containing application data practices. These documents are well-established sources of requirements in software engineering. However, privacy policies are written in natural language, thus subject to ambiguity and abstraction. Eliciting requirements from privacy policies is a challenging task as these ambiguities can result in more than one interpretation of a given information type (e.g., ambiguous information type "device information" in the statement "we collect your device information"). To address this challenge, we propose an automated approach to infer semantic relations among information types and construct an ontology to guide requirements authors in the selection of the most appropriate information type terms. Our solution utilizes word embeddings and Convolutional Neural Networks (CNN) to classify information type pairs as either hypernymy, synonymy, or unknown. We evaluate our model on a manually-built ontology, yielding predictions that identify hypernymy relations in information type pairs with 0.904 F-1 score, suggesting a large reduction in effort required for ontology construction.

Mobile application (app) developers commonly utilize analytic services to analyze their app users' behavior to support debugging, improve service quality, and facilitate advertising. Anonymization and aggregation can reduce the sensitivity of such behavioral data, therefore analytic services often encourage the use of such protections. However, these protections are not directly enforced so it is possible for developers to misconfigure the analytic services and expose personal information, which may cause greater privacy risks. Since people use apps in many aspects of their daily lives, such misconfigurations may lead to the leaking of sensitive personal information such as a users' real-time location, health data, or dating preferences. To study this issue and identify potential privacy risks due to such misconfigurations, we developed a semi-automated approach, Privacy-Aware Analytics Misconfiguration Detector (PAMDroid), which enables our empirical study on mis-configurations of analytic services. This paper describes a study of 1,000 popular apps using top analytic services in which we found misconfigurations in 120 apps. In 52 of the 120 apps, misconfigurations lead to a violation of either the analytic service providers' terms of service or the app's own privacy policy.

Personal data provides important business value, for example, in the personalization of services. In addition, companies are moving toward new business models, in which products and services are offered without charge to users, but in exchange for targeted advertising revenue. New privacy regulations require organizations to explicitly state their data practices in privacy policies, including which data types will be collected. By consenting to data collections described in a policy, the user acknowledges that he or she is granting the company the authorizations needed to access their data. When data practices change, a new version of the policy is released. This release can occur a few times a year, when requirements are rapidly changing for the collection and processing of personal data. Furthermore, the user may change his or her privacy consent by opting in or out of the policy. We propose a formal framework to support companies and users in their understanding of policies evolution under consent regime that supports both retroactive and non-retroactive consent and consent revocation. Preliminary results include an ontology for policy evolution, expressed in Description Logic, that can be used to formalize consent and data collection logs and then query for which data types can be legally accessed.

Advancement in machine learning techniques in recent years has led to deep learning applications on source code. While there is little research available on the subject, the work that has been done shows great potential. We believe deep learning can be leveraged to obtain new insight into automated access control policy verification. In this paper, we describe our first step in applying learning techniques to access control, which consists of developing word embeddings to bootstrap learning tasks. We also discuss the future work on identifying access control enforcement code and checking access control policy violations, which can be enabled by word embeddings.

The Android mobile platform supports billions of devices across more than 190 countries around the world. This popularity coupled with user data collection by Android apps has made privacy protection a well-known challenge in the Android ecosystem. In practice, app producers provide privacy policies disclosing what information is collected and processed by the app. However, it is difficult to trace such claims to the corresponding app code to verify whether the implementation is consistent with the policy. Existing approaches for privacy policy alignment focus on information directly accessed through the Android platform (e.g., location and device ID), but are unable to handle user input, a major source of private information. In this paper, we propose a novel approach that automatically detects privacy leaks of user-entered data for a given Android app and determines whether such leakage may violate the app's privacy policy claims. For evaluation, we applied our approach to 120 popular apps from three privacy-relevant app categories: finance, health, and dating. The results show that our approach was able to detect 21 strong violations and 18 weak violations from the studied apps.

Companies that collect personal information online often maintain privacy policies that are required to accurately reflect their data practices and privacy goals. To be comprehensive and flexible for future practices, policies contain ambiguity that summarize practices over multiple types of products and business contexts. Ambiguity in data practice descriptions undermines policies as an effective way to communicate system design choices to users, and as a reliable regulatory mechanism. In this paper, we report an investigation to identify incompleteness by representing data practice descriptions as semantic frames. The approach is a grounded analysis to discover which data actions and semantic roles correspond are needed to construct complete data practice descriptions. Our results include 281 data action instances obtained from 202 manually annotated statements across five privacy policies. Therein, we identified 878 instances of 17 types of semantic roles. Incomplete data practice descriptions undermine user comprehension, and can affect the user's perceived privacy risk, which we measure using factorial vignette surveys. We observed that user perception of risk decreases when two roles are present in a statement: the condition under which a data action is performed, and the purpose for which the user's information is used.

Personal data is increasingly collected and used by companies to tailor services to users, and to make financial, employment, and health-related decisions about individuals. When personal data is inappropriately collected or misused, however, individuals may experience violations of their privacy. Historically, government regulators have relied on the concept of risk in energy, aviation and medicine, among other domains, to determine the extent to which products and services may harm the public. To address privacy concerns in government-controlled information technology, government agencies are advocating to adapt similar risk management frameworks to privacy. Despite the recent shift toward a risk-managed approach for privacy, to our knowledge, there are no empirical methods to determine which personal data are most at-risk and which contextual factors increase or decrease that risk. To this end, we introduce an empirical framework in this article that consists of factorial vignette surveys that can be used to measure the effect of different factors and their levels on privacy risk. We report a series of experiments to measure perceived privacy risk using the proposed framework, which are based on expressed preferences, and which we define as an individual's willingness to share their personal data with others given the likelihood of a potential privacy harm. These experiments control for one or more of the six factors affecting an individual's willingness to share their information: data type, computer type, data purpose, privacy harm, harm likelihood, and individual demographic factors, such as age range, gender, education level, ethnicity, and household income. To measure likelihood, we introduce and evaluate a new likelihood scale based on construal level theory in psychology. The scale frames individual attitudes about risk likelihood based on social and physical distance to the privacy harm. The findings include predictions about the extent to which the above factors correspond to risk acceptance, including that perceived risk is lower for induced disclosure harms when compared to surveillance and insecurity harms as defined in Solove's Taxonomy of Privacy. We also found that participants are more willing to share their information when they perceive the benefits of sharing. In addition, we found that likelihood was not a multiplicative factor in computing privacy risk perception, which challenges conventional theories of privacy risk in the privacy and security community.

Privacy laws and international privacy standards require that companies collect only the data they have a stated purpose for, called collection limitation. Furthermore, these regimes prescribe that companies will not use data for purposes other than the purposes for which they were collected, called use limitation, except for legal purposes and when the user provides consent. To help companies write better privacy requirements that embody the use limitations and collection limitation principles, we conducted a case study to identify how purpose is expressed among five privacy policies from the shopping domain. Using content analysis, we discovered six exclusive data purpose categories. In addition, we observed natural language patterns to express purpose. Finally, we found that data purpose specificity varies with the specificity of information type descriptions. We believe this taxonomy and the patterns can help policy analysts discover missing or underspecified purposes to better comply with the collection and use limitation principles.

Requirements analysts can model regulated data practices to identify and reason about risks of non-compliance. If terminology is inconsistent or ambiguous, however, these models and their conclusions will be unreliable. To study this problem, we investigated an approach to automatically construct an information type ontology by identifying information type hyponymy in privacy policies using Tregex patterns. Tregex is a utility to match regular expressions against constituency parse trees, which are hierarchical expressions of natural language clauses, including noun and verb phrases. We discovered the Tregex patterns by applying content analysis to 30 privacy policies from six domains (shopping, telecommunication, social networks, employment, health, and news.) From this dataset, three semantic and four lexical categories of hyponymy emerged based on category completeness and word-order. Among these, we identified and empirically evaluated 72 Tregex patterns to automate the extraction of hyponyms from privacy policies. The patterns match information type hyponyms with an average precision of 0.72 and recall of 0.74.

Choosing how to write natural language scenarios is challenging, because stakeholders may over-generalize their descriptions or overlook or be unaware of alternate scenarios. In security, for example, this can result in weak security constraints that are too general, or missing constraints. Another challenge is that analysts are unclear on where to stop generating new scenarios. In this paper, we introduce the Multifactor Quality Method (MQM) to help requirements analysts to empirically collect system constraints in scenarios based on elicited expert preferences. The method combines quantitative statistical analysis to measure system quality with qualitative coding to extract new requirements. The method is bootstrapped with minimal analyst expertise in the domain affected by the quality area, and then guides an analyst toward selecting expert-recommended requirements to monotonically increase system quality. We report the results of applying the method to security. This include 550 requirements elicited from 69 security experts during a bootstrapping stage, and subsequent evaluation of these results in a verification stage with 45 security experts to measure the overall improvement of the new requirements. Security experts in our studies have an average of 10 years of experience. Our results show that using our method, we detect an increase in the security quality ratings collected in the verification stage. Finally, we discuss how our proposed method helps to improve security requirements elicitation, analysis, and measurement.

Mobile applications frequently access sensitive personal information to meet user or business requirements. Because such information is sensitive in general, regulators increasingly require mobile-app developers to publish privacy policies that describe what information is collected. Furthermore, regulators have fined companies when these policies are inconsistent with the actual data practices of mobile apps. To help mobile-app developers check their privacy policies against their apps' code for consistency, we propose a semi-automated framework that consists of a policy terminology-API method map that links policy phrases to API methods that produce sensitive information, and information flow analysis to detect misalignments. We present an implementation of our framework based on a privacy-policy-phrase ontology and a collection of mappings from API methods to policy phrases. Our empirical evaluation on 477 top Android apps discovered 341 potential privacy policy violations.

Ambiguity arises in requirements when a statement is unintentionally or otherwise incomplete, missing information, or when a word or phrase has more than one possible meaning. For web-based and mobile information systems, ambiguity, and vagueness inparticular, undermines the ability of organizations to align their privacy policies with their data practices, which can confuse or mislead users thus leading to an increase in privacy risk. In this paper, we introduce a theory of vagueness for privacy policy statements based on a taxonomy of vague terms derived from an empirical content analysis of 15 privacy policies. The taxonomy was evaluated in a paired comparison experiment and results were analyzed using the Bradley-Terry model to yield a rank order of vague terms in both isolation and composition. The theory predicts how vague modifiers to information actions and information types can be composed to increase or decrease overall vagueness. We further provide empirical evidence based on factorial vignette surveys to show how increases in vagueness will decrease users' acceptance of privacy risk and thus decrease users' willingness to share personal information.

Privacy policies describe high-level goals for corporate data practices; regulators require industries to make available conspicuous, accurate privacy policies to their customers. Consequently, software requirements must conform to those privacy policies. To help stakeholders extract privacy goals from policies, we introduce a semiautomated framework that combines crowdworker annotations, natural language typed dependency parses, and a reusable lexicon to improve goal-extraction coverage, precision, and recall. The framework evaluation consists of a five-policy corpus governing web and mobile information systems, yielding an average precision of 0.73 and recall of 0.83. The results show that no single framework element alone is sufficient to extract goals; however, the overall framework compensates for elemental limitations. Human annotators are highly adaptive at discovering annotations in new texts, but those annotations can be inconsistent and incomplete; dependency parsers lack sophisticated, tacit knowledge, but they can perform exhaustive text search for prospective requirements indicators; and while the lexicon may never completely saturate, the lexicon terms can be reliably used to improve recall. Lexical reuse reduces false negatives by 41%, increasing the average recall to 0.85. Last, crowd workers were able to identify and remove false positives by around 80%, which improves average precision to 0.93.

Mobile and web applications increasingly leverage service-oriented architectures in which developers integrate third-party services into end user applications. This includes identity management, mapping and navigation, cloud storage, and advertising services, among others. While service reuse reduces development time, it introduces new privacy and security risks due to data repurposing and over-collection as data is shared among multiple parties who lack transparency into third-party data practices. To address this challenge, we propose new techniques based on Description Logic (DL) for modeling multiparty data flow requirements and verifying the purpose specification and collection and use limitation principles, which are prominent privacy properties found in international standards and guidelines. We evaluate our techniques in an empirical case study that examines the data practices of the Waze mobile application and three of their service providers: Facebook Login, Amazon Web Services (a cloud storage provider), and Flurry.com (a popular mobile analytics and advertising platform). The study results include detected conflicts and violations of the principles as well as two patterns for balancing privacy and data use flexibility in requirements specifications. Analysis of automation reasoning over the DL models show that reasoning over complex compositions of multi-party systems is feasible within exponential asymptotic timeframes proportional to the policy size, the number of expressed data, and orthogonal to the number of conflicts found.

Security requirements analysis depends on how well-trained analysts perceive security risk, understand the impact of various vulnerabilities, and mitigate threats. When systems are composed of multiple machines, configurations, and software components that interact with each other, risk perception must account for the composition of security requirements. In this paper, we report on how changes to security requirements affect analysts risk perceptions and their decisions about how to modify the requirements to reach adequate security levels. We conducted two user surveys of 174 participants wherein participants assess security levels across 64 factorial vignettes. We analyzed the survey results using multi-level modeling to test for the effect of security requirements composition on participants' overall security adequacy ratings and on their ratings of individual requirements. We accompanied this analysis with grounded analysis of elicited requirements aimed at lowering the security risk. Our results suggest that requirements composition affects experts' adequacy ratings on security requirements. In addition, we identified three categories of requirements modifications, called refinements, replacements and reinforcements, and we measured how these categories compare with overall perceived security risk. Finally, we discuss the future impact of our work in security requirements assessment practice.

Website and mobile application privacy policies are intended to describe the system’s data practices. However, they are often written in non-standard formats and contain ambiguities that make it difficult for users to read and comprehend these documents. We propose a crowdsourcing approach to extract data practices from privacy policies to provide more concise and useable privacy notices to users and support the analysis of stated data practices. To that end, we designed a hierarchical task workflow for crowdsourcing the extraction of data practices from privacy policies. We discuss our workflow design and report preliminary results.

Natural language text sources have increasingly been used to develop new methods and tools for extracting and analyzing requirements. To validate these new approaches, researchers rely on a small number of trained experts to perform a labor-intensive manual analysis of the text. The time and resources needed to conduct manual extraction, however, has limited the size of case studies and thus the generalizability of results. To begin to address this issue, we conducted three experiments to evaluate crowdsourcing a manual requirements extraction task to a larger number of untrained workers. In these experiments, we carefully balance worker payment and overall cost, as well as worker training and data quality to study the feasibility of distributing requirements extraction to the crowd. The task consists of extracting descriptions of data collection, sharing and usage requirements from privacy policies. We present results from two pilot studies and a third experiment to justify applying a task decomposition approach to requirements extraction. Our contributions include the task decomposition workflow and three metrics for measuring worker performance. The final evaluation shows a 60% reduction in the cost of manual extraction with a 16% increase in extraction coverage.

Government laws and regulations increasingly place requirements on software systems. Ideally, experts trained in law will analyze and interpret legal texts to inform the software requirements process. However, in small companies and development teams with short launch cycles, individuals with little or no legal training will be responsible for compliance. Two specific challenges commonly faced by non-experts are deciding if their system is covered by a law, and then deciding whether two legal requirements are similar or different. In this study, we assess the ability of laypersons, technical professionals, and legal experts to judge the similarity between legal coverage conditions and requirements. In so doing, we discovered that legal experts achieved higher rates of consensus more frequently than technical professionals or laypersons and that all groups had slightly greater agreement when judging coverage conditions than requirements, measured by Fleiss' K. When comparing judgments between groups using a consensus-based Cohen's Kappa, we found that technical professionals and legal experts exhibited consistently greater agreement than that found between laypersons and legal experts, and that each group tended towards different justifications, such as laypersons and technical professionals tendency towards categorizing different coverage conditions or requirements as equivalent if they believed them to possess the same underlying intent.

Security requirements patterns represent reusable security practices that software engineers can apply to improve security in their system. Reusing best practices that others have employed could have a number of benefits, such as decreasing the time spent in the requirements elicitation process or improving the quality of the product by reducing product failure risk. Pattern selection can be difficult due to the diversity of applicable patterns from which an analyst has to choose. The challenge is that identifying the most appropriate pattern for a situation can be cumbersome and time-consuming. We propose a new method that combines an inquiry-cycle based approach with the feature diagram notation to review only relevant patterns and quickly select the most appropriate patterns for the situation. Similar to patterns themselves, our approach captures expert knowledge to relate patterns based on decisions made by the pattern user. The resulting pattern hierarchies allow users to be guided through these decisions by questions, which introduce related patterns in order to help the pattern user select the most appropriate patterns for their situation, thus resulting in better requirement generation. We evaluate our approach using access control patterns in a pattern user study.

Companies require data from multiple sources to develop new information systems, such as social networking, e-commerce and location-based services. Systems rely on complex, multi-stakeholder data supply-chains to deliver value. These data supply-chains have complex privacy requirements: privacy policies affecting multiple stakeholders (e.g. user, developer, company, government) regulate the collection, use and sharing of data over multiple jurisdictions (e.g. California, United States, Europe). Increasingly, regulators expect companies to ensure consistency between company privacy policies and company data practices. To address this problem, we propose a methodology to map policy requirements in natural language to a formal representation in Description Logic. Using the formal representation, we reason about conflicting requirements within a single policy and among multiple policies in a data supply chain. Further, we enable tracing data flows within the supply-chain. We derive our methodology from an exploratory case study of Facebook platform policy. We demonstrate the feasibility of our approach in an evaluation involving Facebook, Zynga and AOL-Advertising policies. Our results identify three conflicts that exist between Facebook and Zynga policies, and one conflict within the AOL Advertising policy.

Developing global markets offer companies new opportunities to manufacture and sell information technology (IT) products in ways unforeseen by current laws and regulations. This innovation leads to changing requirements due to changes in product features, laws, or the locality where the product is sold or manufactured. To help developers rationalize these changes, we introduce a preliminary framework and method that can be used by requirements engineers and their legal teams to identify relevant legal requirements and trace changes in requirements coverage. The framework includes a method to translate IT regulations into a legal requirements coverage model used to make coverage assertions about existing or planned IT systems. We evaluated the framework in a case study using three IT laws: California's Confidentiality of Medical Records Act, the U.S. Health Information Portability and Accountability Act (HIPAA) and amendments from the Health Information Technology for Economic and Clinical Health (HITECH) Act, and the India 2011 Information Technology Rules. Further, we demonstrate the framework using three scenarios: new product features are proposed; product-related services are outsourced abroad; and regulations change to address changes in the market.

Companies that own, license, or maintain personal information face a daunting number of privacy and security regulations. Companies are subject to new regulations from one or more governing bodies, when companies introduce new or existing products into a jurisdiction, when regulations change, or when data is transferred across political borders. To address this problem, we developed a framework called “requirements water marking” that business analysts can use to align and reconcile requirements from multiple jurisdictions (municipalities, provinces, nations) to produce a single high or low standard of care. We evaluate the framework in an empirical case study conducted over a subset of U.S. data breach notification laws that require companies to secure their data and notify consumers in the event of data loss or theft. In this study, applying our framework reduced the number of requirements a company must comply with by 76% across 8 jurisdictions. We show how the framework surfaces critical requirements trade-offs and potential regulatory conflicts that companies must address during the reconciliation process. We summarize our results, including surveys of information technology law experts to contextualize our empirical results in legal practice.

Government laws and organizational policies introduce critical legal requirements that govern information systems. Unlike traditional requirements elicited from stakeholders, legal requirements have unique characteristics that software engineers must address to ensure that their systems are demonstrably compliant with relevant laws and policies. This paper presents important terminology for developing legally compliant software systems and a methodology consisting of procedures and models for acquiring, representing and analyzing phenomena in legal documents, which constitute rich sources of legal requirements. Based on a grounded theory, the method has been validated through a mixed-methods approach consisting of multiple, descriptive case studies. This paper presents a human subject experiment that tests a fundamental part of the theory to understand the efficacy of multiple users applying the method to a sample regulation text.

U.S. laws and regulations are designed to support broad societal goals, such as accessibility, privacy and safety. To demonstrate that a product complies with these goals, businesses need to identify and refine legal requirements into product requirements and integrate the product requirements into their ongoing product design and testing processes. We report on an industry case study in which product requirements were speci-fied to comply with Section 508 of the U.S. Rehabilitation Act Amendments of 1998. This study sought to identify: limitations in existing requirements-acquisition methods; compliance gaps between previ-ously specified product requirements and Section 508; and additional sources of knowledge that are neces-sary to refine legal requirements into product require-ments to comply with the law. Our study reveals the need for a community of practice and generalizable techniques that can reduce ambiguity, complexity and redundancy in legal and product requirements and manage innovation in product requirements. We pre-sent these findings with several examples from Section 508 regulations and actual product requirements that are implemented in Cisco products.

Software engineers must systematically account for the broad scope of environmental behavior, including nonfunctional requirements, intended to coordinate the actions of stakeholders and software systems. The Inquiry Cycle Model (ICM) provides engineers with a strategy to acquire and refine these requirements by having domain experts answer six questions: who, what, where, when, how, and why. Goal-based requirements engineering has led to the formalization of requirements to answer the ICM questions about when , how , and why goals are achieved, maintained, or avoided. In this article, we present a systematic process called Semantic Parameterization for expressing natural language domain descriptions of goals as specifications in description logic. The formalization of goals in description logic allows engineers to automate inquiries using who , what , and where questions, completing the formalization of the ICM questions. The contributions of this approach include new theory to conceptually compare and disambiguate goal specifications that enables querying goals and organizing goals into specialization hierarchies. The artifacts in the process include a dictionary that aligns the domain lexicon with unique concepts, distinguishing between synonyms and polysemes, and several natural language patterns that aid engineers in mapping common domain descriptions to formal specifications. Semantic Parameterization has been empirically validated in three case studies on policy and regulatory descriptions that govern information systems in the finance and health-care domains.

Information practices that use personal, financial and health-related information are governed by U.S. laws and regulations to prevent unauthorized use and disclosure. To ensure compliance under the law, the security and privacy requirements of relevant software systems must be properly aligned with these regulations. However, these regulations describe stakeholder rules, called rights and obligations, in complex and sometimes ambiguous legal language. These "rules" are often precursors to software requirements that must undergo considerable refinement and analysis before they are implementable. To support the software engineering effort to derive security requirements from regulations, we present a methodology to extract access rights and obligations directly from regulation texts. The methodology provides statement-level coverage for an entire regulatory document to consistently identify and infer six types of data access constraints, handle complex cross-references, resolve ambiguities, and assign required priorities between access rights and obligations to avoid unlawful information disclosures. We present results from applying this methodology to the entire regulation text of the U.S. Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule.

Security, privacy and governance are increasingly the focus of government regulations in the U.S., Europe and elsewhere. This trendhas created a "regulation compliance problem", whereby companiesand developers are required to ensure that their software complies with relevant regulations, either through design or reengineering. We previously proposed a methodology for extracting stakeholder requirements, called rights and obligations, from regulations. In this paper, we examine the challenges of developing tool support for this process. We apply the Cerno framework for textual semantic annotation to propose a tool for semi-automatic semantic annotation of concepts that constitute sources of requirements

In the United States, federal and state regulations prescribe stakeholder rights and obligations that must be satisfied by the requirements for software systems. These regulations are typically wrought with ambiguities, making the process of deriving system requirements ad hoc and error prone. In highly regulated domains such as healthcare, there is a need for more comprehensive standards that can be used to assure that system requirements conform to regulations. To address this need, we expound upon a process called semantic parameterization previously used to derive rights and obligations from privacy goals. In this work, we apply the process to the privacy rule from the U.S. Health Insurance Portability and Accountability Act (HIPAA). We present our methodology for extracting and prioritizing rights and obligations from regulations and show how semantic models can be used to clarify ambiguities through focused elicitation and to balance rights with obligations. The results of our analysis can aid requirements engineers, standards organizations, compliance officers, and stakeholders in assuring systems conform to policy and satisfy requirements

Software requirements, rights, permissions, obligations, and operations of policy enforcing systems are often misaligned. Our goal is to develop tools and techniques that help requirements engineers and policy makers bring policies and system requirements into better alignment. Goals from requirements engineering are useful for distilling natural language policy statements into structured descriptions of these interactions; however, they are limited in that they are not easy to compare with one another despite sharing common semantic features. In this paper, we describe a process called semantic parameterization that we use to derive semantic models from goals mined from privacy policy documents. We present example semantic models that enable comparing policy statements and present a template method for generating natural language policy statements (and ultimately requirements) from unique semantic models. The semantic models are described by a context-free grammar called KTL that has been validated within the context of the most frequently expressed goals in over 100 Internet privacy policy documents. KTL is supported by a policy analysis tool that supports queries and policy statement generation.