References of "Nejati, Shiva 50002745"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailAn Active Learning Approach for Improving the Accuracy of Automated Domain Model Extraction
Arora, Chetan UL; Sabetzadeh, Mehrdad UL; Nejati, Shiva UL et al

in ACM Transactions on Software Engineering and Methodology (in press)

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to ... [more ▼]

Domain models are a useful vehicle for making the interpretation and elaboration of natural-language requirements more precise. Advances in natural language processing (NLP) have made it possible to automatically extract from requirements most of the information that is relevant to domain model construction. However, alongside the relevant information, NLP extracts from requirements a significant amount of information that is superfluous, i.e., not relevant to the domain model. Our objective in this article is to develop automated assistance for filtering the superfluous information extracted by NLP during domain model extraction. To this end, we devise an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements, and uses this feedback to provide recommendations for filtering superfluous elements. We empirically evaluate our approach over three industrial case studies. Our results indicate that, once trained, our approach automatically detects an average of ≈ 45% of the superfluous elements with a precision of ≈ 96%. Since precision is very high, the automatic recommendations made by our approach are trustworthy. Consequently, analysts can dispose of a considerable fraction – nearly half – of the superfluous elements with minimal manual work. The results are particularly promising, as they should be considered in light of the non-negligible subjectivity that is inherently tied to the notion of relevance. [less ▲]

Detailed reference viewed: 77 (9 UL)
Full Text
Peer Reviewed
See detailHITECS: A UML Profile and Analysis Framework for Hardware-in-the-Loop Testing of Cyber Physical Systems
Shin, Seung Yeob UL; Chaouch, Karim UL; Nejati, Shiva UL et al

in Proceedings of ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS’18) (2018, October)

Hardware-in-the-loop (HiL) testing is an important step in the development of cyber physical systems (CPS). CPS HiL test cases manipulate hardware components, are time-consuming and their behaviors are ... [more ▼]

Hardware-in-the-loop (HiL) testing is an important step in the development of cyber physical systems (CPS). CPS HiL test cases manipulate hardware components, are time-consuming and their behaviors are impacted by the uncertainties in the CPS environment. To mitigate the risks associated with HiL testing, engineers have to ensure that (1) HiL test cases are well-behaved, i.e., they implement valid test scenarios and do not accidentally damage hardware, and (2) HiL test cases can execute within the time budget allotted to HiL testing. This paper proposes an approach to help engineers systematically specify and analyze CPS HiL test cases. Leveraging the UML profile mechanism, we develop an executable domain-specific language, HITECS, for HiL test case specification. HITECS builds on the UML Testing Profile (UTP) and the UML action language (Alf). Using HITECS, we provide analysis methods to check whether HiL test cases are well-behaved, and to estimate the execution times of these test cases before the actual HiL testing stage. We apply HITECS to an industrial case study from the satellite domain. Our results show that: (1) HITECS is feasible to use in practice; (2) HITECS helps engineers define more complete and effective well-behavedness assertions for HiL test cases, compared to when these assertions are defined without systematic guidance; (3) HITECS verifies in practical time that HiL test cases are well-behaved; and (4) HITECS accurately estimates HiL test case execution times. [less ▲]

Detailed reference viewed: 150 (29 UL)
Full Text
Peer Reviewed
See detailEnabling Model Testing of Cyber-Physical Systems
Gonzalez Perez, Carlos Alberto UL; Varmazyar, Mojtaba UL; Nejati, Shiva UL et al

in Proceedings of ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS’18) (2018, October)

Applying traditional testing techniques to Cyber-Physical Systems (CPS) is challenging due to the deep intertwining of software and hardware, and the complex, continuous interactions between the system ... [more ▼]

Applying traditional testing techniques to Cyber-Physical Systems (CPS) is challenging due to the deep intertwining of software and hardware, and the complex, continuous interactions between the system and its environment. To alleviate these challenges we propose to conduct testing at early stages and over executable models of the system and its environment. Model testing of CPSs is however not without difficulties. The complexity and heterogeneity of CPSs renders necessary the combination of different modeling formalisms to build faithful models of their different components. The execution of CPS models thus requires an execution framework supporting the co-simulation of different types of models, including models of the software (e.g., SysML), hardware (e.g., SysML or Simulink), and physical environment (e.g., Simulink). Furthermore, to enable testing in realistic conditions, the co-simulation process must be (1) fast, so that thousands of simulations can be conducted in practical time, (2) controllable, to precisely emulate the expected runtime behavior of the system and, (3) observable, by producing simulation data enabling the detection of failures. To tackle these challenges, we propose a SysML-based modeling methodology for model testing of CPSs, and an efficient SysML-Simulink co-simulation framework. Our approach was validated on a case study from the satellite domain. [less ▲]

Detailed reference viewed: 115 (17 UL)
Full Text
Peer Reviewed
See detailSoftware Engineering Research and Industry: A Symbiotic Relationship to Foster Impact
Basili, Victor; Briand, Lionel UL; Bianculli, Domenico UL et al

in IEEE Software (2018), 35(5), 44-49

Software engineering is not only an increasingly challenging endeavor that goes beyond the intellectual capabilities of any single individual engineer, but is also an intensely human one. Tools and ... [more ▼]

Software engineering is not only an increasingly challenging endeavor that goes beyond the intellectual capabilities of any single individual engineer, but is also an intensely human one. Tools and methods to develop software are employed by engineers of varied backgrounds within a large variety of organizations and application domains. As a result, the variation in challenges and practices in system requirements, architecture, and quality assurance is staggering. Human, domain and organizational factors define the context within which software engineering methodologies and technologies are to be applied and therefore the context that research needs to account for, if it is to be impactful. This paper provides an assessment of the current challenges faced by software engineering research in achieving its potential, a description of the root causes of such challenges, and a proposal for the field to move forward and become more impactful through collaborative research and innovation between public research and industry. [less ▲]

Detailed reference viewed: 75 (11 UL)
Full Text
Peer Reviewed
See detailTest Case Prioritization for Acceptance Testing of Cyber Physical Systems: A Multi-Objective Search-Based Approach
Shin, Seung Yeob UL; Nejati, Shiva UL; Sabetzadeh, Mehrdad UL et al

in Proceedings of the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'18) (2018, July)

Acceptance testing validates that a system meets its requirements and determines whether it can be sufficiently trusted and put into operation. For cyber physical systems (CPS), acceptance testing is a ... [more ▼]

Acceptance testing validates that a system meets its requirements and determines whether it can be sufficiently trusted and put into operation. For cyber physical systems (CPS), acceptance testing is a hardware-in-the-loop process conducted in a (near-)operational environment. Acceptance testing of a CPS often necessitates that the test cases be prioritized, as there are usually too many scenarios to consider given time constraints. CPS acceptance testing is further complicated by the uncertainty in the environment and the impact of testing on hardware. We propose an automated test case prioritization approach for CPS acceptance testing, accounting for time budget constraints, uncertainty, and hardware damage risks. Our approach is based on multi-objective search, combined with a test case minimization algorithm that eliminates redundant operations from an ordered sequence of test cases. We evaluate our approach on a representative case study from the satellite domain. The results indicate that, compared to test cases that are prioritized manually by satellite engineers, our automated approach more than doubles the number of test cases that fit into a given time frame, while reducing to less than one third the number of operations that entail the risk of damage to key hardware components. [less ▲]

Detailed reference viewed: 178 (19 UL)
Full Text
Peer Reviewed
See detailTesting Vision-Based Control Systems Using Learnable Evolutionary Algorithms
Ben Abdessalem (helali), Raja UL; Nejati, Shiva UL; Briand, Lionel UL et al

in Proceedings of the 40th International Conference on Software Engineering (ICSE 2018) (2018)

Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We ... [more ▼]

Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive vision-based control system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that likely to lead to system failures. [less ▲]

Detailed reference viewed: 475 (106 UL)
Full Text
Peer Reviewed
See detailTest Generation and Test Prioritization for Simulink Models with Dynamic Behavior
Matinnejad, Reza; Nejati, Shiva UL; Briand, Lionel UL et al

in IEEE Transactions on Software Engineering (2018)

All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Among the different disciplines, the engineering of Cyber Physical Systems (CPSs ... [more ▼]

All engineering disciplines are founded and rely on models, although they may differ on purposes and usages of modeling. Among the different disciplines, the engineering of Cyber Physical Systems (CPSs) particularly relies on models with dynamic behaviors (i.e., models that exhibit time-varying changes). The Simulink modeling platform greatly appeals to CPS engineers since it captures dynamic behavior models. It further provides seamless support for two indispensable engineering activities: (1) automated verification of abstract system models via model simulation, and (2) automated generation of system implementation via code generation. We identify three main challenges in the verification and testing of Simulink models with dynamic behavior, namely incompatibility, oracle and scalability challenges. We propose a Simulink testing approach that attempts to address these challenges. Specifically, we propose a black-box test generation approach, implemented based on meta-heuristic search, that aims to maximize diversity in test output signals generated by Simulink models. We argue that in the CPS domain test oracles are likely to be manual and therefore the main cost driver of testing. In order to lower the cost of manual test oracles, we propose a test prioritization algorithm to automatically rank test cases generated by our test generation algorithm according to their likelihood to reveal a fault. Engineers can then select, according to their test budget, a subset of the most highly ranked test cases. To demonstrate scalability, we evaluate our testing approach using industrial Simulink models. Our evaluation shows that our test generation and test prioritization approaches outperform baseline techniques that rely on random testing and structural coverage. [less ▲]

Detailed reference viewed: 110 (23 UL)
Full Text
Peer Reviewed
See detailEffective Fault Localization of Automotive Simulink Models: Achieving the Trade-Off between Test Oracle Effort and Fault Localization Accuracy
Liu, Bing; Nejati, Shiva UL; Lucia, Lucia et al

in Empirical Software Engineering (2018)

One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding ... [more ▼]

One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding test cases is not a cost-free option because test oracles are developed manually or running test cases is expensive. Hence, we require to have test suites that are both diverse and small to improve debugging. In this paper, we focus on improving fault localization of Simulink models by generating test cases. We identify four test objectives that aim to increase test suite diversity. We use four objectives in a search-based algorithm to generate diversified but small test suites. To further minimize test suite sizes, we develop a prediction model to stop test generation when adding test cases is unlikely to improve fault localization. We evaluate our approach using three industrial subjects. Our results show (1) expanding test suites used for fault localization using any of our four test objectives, even when the expansion is small, can significantly improve the accuracy of fault localization, (2) varying test objectives used to generate the initial test suites for fault localization does not have a significant impact on the fault localization results obtained based on those test suites, and (3) we identify an optimal configuration for prediction models to help stop test generation when it is unlikely to be beneficial. We further show that our optimal prediction model is able to maintain almost the same fault localization accuracy while reducing the average number of newly generated test cases by more than half. [less ▲]

Detailed reference viewed: 136 (13 UL)
Full Text
Peer Reviewed
See detailTesting Autonomous Cars for Feature Interaction Failures using Many-Objective Search
Ben Abdessalem (helali), Raja UL; Panichella, Annibale; Nejati, Shiva UL et al

in Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018) (2018)

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ... [more ▼]

Complex systems such as autonomous cars are typically built as a composition of features that are independent units of functionality. Features tend to interact and impact one another’s behavior in unknown ways. A challenge is to detect and manage feature interactions, in particular, those that violate system requirements, hence leading to failures. In this paper, we propose a technique to detect feature interaction failures by casting our approach into a search-based test generation problem. We define a set of hybrid test objectives (distance functions) that combine traditional coverage-based heuristics with new heuristics specifically aimed at revealing feature interaction failures. We develop a new search-based test generation algorithm, called FITEST, that is guided by our hybrid test objectives. FITEST extends recently proposed many-objective evolutionary algorithms to reduce the time required to compute fitness values. We evaluate our approach using two versions of an industrial self-driving system. Our results show that our hybrid test objectives are able to identify more than twice as many feature interaction failures as two baseline test objectives used in the software testing literature (i.e., coverage-based and failure-based test objectives). Further, the feedback from domain experts indicates that the detected feature interaction failures represent real faults in their systems that were not previously identified based on analysis of the system features and their requirements. [less ▲]

Detailed reference viewed: 180 (15 UL)
Full Text
Peer Reviewed
See detailThe Case for Context-Driven Software Engineering Research
Briand, Lionel UL; Bianculli, Domenico UL; Nejati, Shiva UL et al

in IEEE Software (2017), 34(5), 72-75

Detailed reference viewed: 273 (26 UL)
Full Text
Peer Reviewed
See detailAutomated Testing of Hybrid Simulink/Stateflow Controllers: Industrial Case Studies
Matinnejad, Reza UL; Nejati, Shiva UL; Briand, Lionel UL

in Proceedings of 11TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE 2017) (2017)

We present the results of applying our approach for testing Simulink controllers to one public and one proprietary model, both industrial. Our approach combines explorative and exploitative search ... [more ▼]

We present the results of applying our approach for testing Simulink controllers to one public and one proprietary model, both industrial. Our approach combines explorative and exploitative search algorithms to visualize the controller behavior over its input space and to identify test scenarios in the controller input space that violate or are likely to violate the controller requirements. The engineers' feedback shows that our approach is easy to use in practice and gives them confidence about the behavior of their models. [less ▲]

Detailed reference viewed: 202 (22 UL)
Full Text
Peer Reviewed
See detailImproving Fault Localization for Simulink Models using Search-Based Testing and Prediction Models
Liu, Bing UL; Lucia, Lucia; Nejati, Shiva UL et al

in 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2017) (2017)

One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding ... [more ▼]

One promising way to improve the accuracy of fault localization based on statistical debugging is to increase diversity among test cases in the underlying test suite. In many practical situations, adding test cases is not a cost-free option because test oracles are developed manually or running test cases is expensive. Hence, we require to have test suites that are both diverse and small to improve debugging. In this paper, we focus on improving fault localization of Simulink models by generating test cases. We identify three test objectives that aim to increase test suite diversity. We use these objectives in a search-based algorithm to generate diversified but small test suites. To further minimize test suite sizes, we develop a prediction model to stop test generation when adding test cases is unlikely to improve fault localization. We evaluate our approach using three industrial subjects. Our results show (1) the three selected test objectives are able to significantly improve the accuracy of fault localization for small test suite sizes, and (2) our prediction model is able to maintain almost the same fault localization accuracy while reducing the average number of newly generated test cases by more than half. [less ▲]

Detailed reference viewed: 290 (43 UL)
Full Text
Peer Reviewed
See detailSimulink Fault Localisation: an Iterative Statistical Debugging Approach
Liu, Bing UL; Lucia, Lucia UL; Nejati, Shiva UL et al

in Software Testing, Verification & Reliability (2016), 26(6), 431-459

Debugging Simulink models presents a significant challenge in the embedded industry. In this work, we propose SimFL, a fault localization approach for Simulink models by combining statistical debugging ... [more ▼]

Debugging Simulink models presents a significant challenge in the embedded industry. In this work, we propose SimFL, a fault localization approach for Simulink models by combining statistical debugging and dynamic model slicing. Simulink models, being visual and hierarchical, have multiple outputs at different hierarchy levels. Given a set of outputs to observe for localizing faults, we generate test execution slices, for each test case and output, of the Simulink model. In order to further improve fault localization accuracy, we propose iSimFL, an iterative fault localization algorithm. At each iteration, iSimFL increases the set of observable outputs by including outputs at lower hierarchy levels, thus increasing the test oracle cost but offsetting it with significantly more precise fault localization. We utilize a heuristic stopping criterion to avoid unnecessary test oracle extension. We evaluate our work on three industrial Simulink models from Delphi Automotive. Our results show that, on average, SimFL ranks faulty blocks in the top 8.9% in the list of suspicious blocks. Further, we show that iSimFL significantly improves this percentage down to 4.4% by requiring engineers to observe only an average of five additional outputs at lower hierarchy levels on top of high-level model outputs. [less ▲]

Detailed reference viewed: 251 (55 UL)
Full Text
Peer Reviewed
See detailTesting the Untestable: Model Testing of Complex Software-Intensive Systems
Briand, Lionel UL; Nejati, Shiva UL; Sabetzadeh, Mehrdad UL et al

in Proceedings of the 38th International Conference on Software Engineering (ICSE 2016) Companion (2016, May)

Detailed reference viewed: 496 (45 UL)
Full Text
Peer Reviewed
See detailAutomated Test Suite Generation for Time-Continuous Simulink Models
Matinnejad, Reza UL; Nejati, Shiva UL; Briand, Lionel UL et al

in Proceedings of the 38th International Conference on Software Engineering (2016)

All engineering disciplines are founded and rely on models, al- though they may differ on purposes and usages of modeling. Inter- disciplinary domains such as Cyber Physical Systems (CPSs) seek approaches ... [more ▼]

All engineering disciplines are founded and rely on models, al- though they may differ on purposes and usages of modeling. Inter- disciplinary domains such as Cyber Physical Systems (CPSs) seek approaches that incorporate different modeling needs and usages. Specifically, the Simulink modeling platform greatly appeals to CPS engineers due to its seamless support for simulation and code generation. In this paper, we propose a test generation approach that is applicable to Simulink models built for both purposes of simulation and code generation. We define test inputs and outputs as signals that capture evolution of values over time. Our test gener- ation approach is implemented as a meta-heuristic search algorithm and is guided to produce test outputs with diverse shapes according to our proposed notion of diversity. Our evaluation, performed on industrial and public domain models, demonstrates that: (1) In con- trast to the existing tools for testing Simulink models that are only applicable to a subset of code generation models, our approach is applicable to both code generation and simulation Simulink mod- els. (2) Our new notion of diversity for output signals outperforms random baseline testing and an existing notion of signal diversity in revealing faults in Simulink models. (3) The fault revealing ability of our test generation approach outperforms that of the Simulink Design Verifier, the only testing toolbox for Simulink. [less ▲]

Detailed reference viewed: 264 (32 UL)
Full Text
Peer Reviewed
See detailTesting Advanced Driver Assistance Systems using Multi-objective Search and Neural Networks
Ben Abdessalem (helali), Raja UL; Nejati, Shiva UL; Briand, Lionel UL et al

in International Conference on Automated Software Engineering (ASE 2016) (2016)

Recent years have seen a proliferation of complex Advanced Driver Assistance Systems (ADAS), in particular, for use in autonomous cars. These systems consist of sensors and cameras as well as image ... [more ▼]

Recent years have seen a proliferation of complex Advanced Driver Assistance Systems (ADAS), in particular, for use in autonomous cars. These systems consist of sensors and cameras as well as image processing and decision support software components. They are meant to help drivers by providing proper warnings or by preventing dangerous situations. In this paper, we focus on the problem of design time testing of ADAS in a simulated environment. We provide a testing approach for ADAS by combining multi- objective search with surrogate models developed based on neural networks. We use multi-objective search to guide testing towards the most critical behaviors of ADAS. Surrogate modeling enables our testing approach to explore a larger part of the input search space within limited computational resources. We characterize the condition under which the multi-objective search algorithm behaves the same with and without surrogate modeling, thus showing the accuracy of our approach. We evaluate our approach by applying it to an industrial ADAS system. Our experiment shows that our approach automatically identifies test cases indicating critical ADAS behaviors. Further, we show that combining our search algorithm with surrogate modeling improves the quality of the generated test cases, especially under tight and realistic computational resources. [less ▲]

Detailed reference viewed: 473 (111 UL)
Full Text
Peer Reviewed
See detailLocalizing Multiple Faults in Simulink Models.
Liu, Bing UL; Lucia, Lucia UL; Nejati, Shiva UL et al

in 23rd IEEE International Conference on Software Analysis, Evolution, and Reengineering (SANER 2016) (2016)

As Simulink is a widely used language in the embedded industry, there is a growing need to support debugging activities for Simulink models. In this work, we propose an approach to localize multiple ... [more ▼]

As Simulink is a widely used language in the embedded industry, there is a growing need to support debugging activities for Simulink models. In this work, we propose an approach to localize multiple faults in Simulink models. Our approach builds on statistical debugging and is iterative. At each iteration, we identify and resolve one fault and re-test models to focus on localizing faults that might have been masked before. We use decision trees to cluster together failures that satisfy similar (logical) conditions on model blocks or inputs. We then present two alternative selection criteria to choose a cluster that is more likely to yield the best fault localization results among the clusters produced by our decision trees. Engineers are expected to inspect the ranked list obtained from the selected cluster to identify faults. We evaluate our approach on 240 multi-fault models obtained from three different industrial subjects. We compare our approach with two baselines: (1) Statistical debugging without clustering, and (2) State-of-the-art clustering-based statistical debugging. Our results show that our approach significantly reduces the number of blocks that engineers need to inspect in order to localize all faults, when compared with the two baselines. Furthermore, with our approach, there is less performance degradation than in the baselines when increasing the number of faults in the underlying models. [less ▲]

Detailed reference viewed: 263 (41 UL)
Full Text
Peer Reviewed
See detailSimCoTest: A Test Suite Generation Tool for Simulink/Stateflow Controllers
Matinnejad, Reza UL; Nejati, Shiva UL; Briand, Lionel UL et al

in Proceedings of the 38th International Conference on Software Engineering (2016)

Detailed reference viewed: 216 (24 UL)
Full Text
Peer Reviewed
See detailAutomated Change Impact Analysis between SysML Models of Requirements and Design
Nejati, Shiva UL; Sabetzadeh, Mehrdad UL; Arora, Chetan UL et al

in 24th ACM SIGSOFT International Symposium on the Foundations of Software Engineering, Seattle 13-18 November 2016 (2016)

An important activity in systems engineering is analyzing how a change in requirements will impact the design of a system. Performing this analysis manually is expensive, particularly for complex systems ... [more ▼]

An important activity in systems engineering is analyzing how a change in requirements will impact the design of a system. Performing this analysis manually is expensive, particularly for complex systems. In this paper, we propose an approach to automatically identify the impact of requirements changes on system design, when the requirements and design elements are expressed using models. We ground our approach on the Systems Modeling Language (SysML) due to SysML’s increasing use in industrial applications. Our approach has two steps: For a given change, we first apply a static slicing algorithm to extract an estimated set of impacted model elements. Next, we rank the elements of the resulting set according to a quantitative measure designed to predict how likely it is for each element to be impacted. The measure is computed using Natural Language Processing (NLP) applied to the textual content of the elements. Engineers can then inspect the ranked list of elements and identify those that are actually impacted. We evaluate our approach on an industrial case study with 16 real-world requirements changes. Our results suggest that, using our approach, engineers need to inspect on average only 4.8% of the entire design in order to identify the actually-impacted elements. We further show that our results consistently improve when our analysis takes into account both structural and behavioral diagrams rather than only structural ones, and the natural-language content of the diagrams in addition to only their structural and behavioral content. [less ▲]

Detailed reference viewed: 220 (27 UL)