@misc {26955,
	title = {Enabling automated requirements reuse and configuration},
	howpublished = {23rd International Systems and Software Product Line Conference, Paris, France},
	year = {2019},
	publisher = {ACM},
	type = {Paris, France},
	doi = {https://doi.org/10.1145/3336294.3342370},
	author = {Li, Yan and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@article {26077,
	title = {Empirical Research in Software Engineering - a Literature Survey},
	journal = {Journal of Computer Science and Technology},
	volume = {33},
	year = {2018},
	month = {09/2018},
	pages = {876-899},
	publisher = {Springer},
	doi = {10.1007/s11390-018-1864-x},
	author = {Zhang, Li and Tian, Jia-Hao and Jiang, Jing and Liu, Yi-Jun and Pu, Meng-Yuan and Yue, Tao}
}
@article {25646,
	title = {Enabling Automated Requirements Reuse and Configuration},
	journal = {Software and Systems Modeling},
	year = {2017},
	publisher = {Springer},
	address = {Berlin Heidelberg},
	issn = {1619-1374},
	doi = {10.1007/s10270-017-0641-6},
	author = {Li, Yan and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@inproceedings {25340,
	title = {A Multi-objective and Cost-Aware Optimization of Requirements Assignment},
	journal = {EEE Congress on Evolutionary Computation 2017 (CEC)},
	year = {2017},
	publisher = {IEEE},
	abstract = {A typical way to improve the quality of requirements is to assign them to suitable stakeholders for reviewing. Due to different characteristics of requirements and diverse background of stakeholders, it is needed to find an optimal solution for requirements assignment. Existing search-based requirements assignment solutions focus on maximizing stakeholders{\textquoteright} familiarities to assigned requirements and balancing the overall workload of each stakeholder. However, a cost-effective requirements assignment solution should also take into account another two optimization objectives: 1) minimizing required time for re-viewing requirements, and 2) minimizing the monetary cost required for performing reviewing tasks. We formulated the requirements assignment problem as a search problem and defined a fitness function considering all the five optimization objectives. We conducted an empirical evaluation to assess the fitness function together with six search algorithms using a real-world case study and 120 artificial problems to assess the scalability of the proposed fitness function. Results show that overall, our optimization problem is complex and further justifies the use for multi-objectives search algorithms, and the Speed-constrained Multi-Objective Particle Swarm Optimization (SMPSO) algorithm performed the best among all the search algorithms.},
	author = {Li, Yan and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@article {24849,
	title = {Reliability-Redundancy-Location Allocation with Maximum Reliability and Minimum Cost Using Search Technique},
	journal = {Information and Software Techonology},
	volume = {82},
	year = {2017},
	pages = {36-54},
	publisher = {Elsevier},
	issn = {0950-5849},
	doi = {http://dx.doi.org/10.1016/j.infsof.2016.09.010},
	author = {Qiu, Xiang and Ali, Shaukat and Yue, Tao and Zhang, Li}
}
@inproceedings {25519,
	title = {Search-based Uncertainty-wise Requirements Prioritization},
	journal = {The 22nd International Conference on Engineering of Complex Computer Systems},
	year = {2017},
	publisher = {IEEE},
	abstract = {To ensure the quality of requirements, a common practice, especially in critical domains, is to review requirements within a limited time and monetary budgets. A requirement with higher importance, larger number of dependencies with other requirements, and higher implementation cost should be re-viewed with the highest priority. However, requirements are inherently uncertain in terms of their impact on the requirements implementation cost. Such cost is typically estimated by stakeholders as an interval, though an exact value is often used in the literature for requirements optimization (e.g., prioritization). Such a practice, therefore, ignores uncertainty inherent in the estimation of requirements implementation cost. This paper explicitly taken into account such uncertainty for requirement prioritization and formulates four objectives for uncertainty-wise requirements prioritization with the aim of maximizing 1) the importance of requirements, 2) requirements dependencies, 3) the implementation cost of requirements, and 4) cost over-run probability. We evaluated the multi-objective search algorithm NSGA-II together with Random Search (RS) using the RALIC dataset and 19 artificial problems. Results show that NSGA-II can solve the requirements prioritization problem with a significantly better performance than RS. Moreover, NSGA-II can prioritize requirements with higher priority earlier in the prioritization sequence. For example, in the case of the RALIC dataset, the first 10\% of prioritized requirements in the prioritization sequence are on average 50\% better than RS in terms of prioritization effectiveness.},
	author = {Li, Yan and Zhang, Man and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@techreport {25444,
	title = {Search-based Uncertainty-wise Requirements Prioritization},
	year = {2017},
	publisher = {Simula Research Laboratory},
	abstract = {To ensure the quality of requirements, a common practice, especially in critical domains, is to review requirements within a limited time and monetary budgets. A requirement with higher importance, larger number of dependencies with other requirements, and higher implementation cost should be reviewed with the highest priority. However, requirements are inherently uncertain in terms of their impact on the requirements implementation cost. Such cost is typically estimated by stakeholders as an interval, though an exact value is often used in the literature for requirements optimization (e.g., prioritization). Such a practice, therefore, ignores uncertainty inherent in the estimation of requirements implementation cost. This paper explicitly taken into account such uncertainty for requirement prioritization and formulates four objectives for uncertainty-wise requirements prioritization with the aim of maximizing 1) the importance of requirements, 2) requirements dependencies, 3) the implementation cost of requirements, and 4) cost overrun probability. We evaluated the multi-objective search algorithm NSGA-II together with Random Search (RS) using the RALIC dataset and 19 artificial problems. Results show that NSGA-II can solve the requirements prioritization problem with a significantly better performance than RS. Moreover, NSGA-II can prioritize requirements with higher priority earlier in the prioritization sequence. For example, in the case of the RALIC dataset, the first 10\% of prioritized requirements in the prioritization sequence are on average 50\% better than RS in terms of prioritization effectiveness.},
	author = {Li, Yan and Zhang, Man and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@article {24588,
	title = {Model-based Incremental Conformance Checking to Enable Interactive Product Configuration},
	journal = {Information and Software Technology},
	volume = {72},
	year = {2016},
	month = {04/2016},
	pages = {68-89},
	publisher = {Elsevier},
	abstract = {Context: Model-based product line engineering (PLE) is a paradigm that can enable automated product configuration of large- scale software systems, in which models are used as an abstract specification of commonalities and variabilities of products of a product line. Objective: In the context of PLE, providing immediate feedback on the correctness of a manual configuration step to users has a practical impact on whether a configuration process with tool support can be successfully adopted in practice. Method: In an existing work, an UML-based variability modeling methodology named as SimPL and an interactive configuration process was proposed. Based on the existing work, we propose an automated, incremental and efficient conformance checking approach to ensure that the manual configuration of a variation point conforms to a set of pre-defined conformance rules specified in the Object Constraint Language (OCL). The proposed approach, named as Zen-CC, has been implemented as an integrated part of our product configuration and derivation tool: Zen-Configurator. Results: The performance and scalability of Zen-CC have been evaluated with a real-world case study. Results show that Zen- CC significantly outperformed two baseline engines in terms of performance. Besides, the performance of Zen-CC remains stable during the configuration of all the 10 products of the product line and its efficiency also remains un-impacted even with the growing product complexity, which is not the case for both of the baseline engines. Conclusion: The results suggest that Zen-CC performs practically well and is much more scalable than the two baseline engines and is scalable for configuring products with a larger number of variation points.\&nbsp;},
	author = {Lu, Hong and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@inproceedings {23904,
	title = {Nonconformity Resolving Recommendations for Product Line Configuration},
	journal = {IEEE International Conference on Software Testing, Verification and Validation (ICST)},
	year = {2016},
	month = {04/2016},
	abstract = {In the context of large-scale system product line engineering, manual configuration is often mandatory and therefore inevitably introduces nonconformities: violating pre-defined constraints for conformance checking. Resolving nonconformities without proper tool support is more or less random, as there are usually hundreds and thousands of configurable parameters and conformance constraints, in the context of configuring a large-scale and directly deployable system. Moreover, inter-connections among constraints and configurable parameters worsen the feasibility of manual resolving nonconformities without proper tool support. In this paper, we present an automatic approach (named as Zen-FIX) to optimally recommend solutions to resolve nonconformities by combining multi-objective search and constraint solving techniques. Solutions recommended by Zen-FIX conform to all pre-defined constraints and are optimal in terms of maximizing the overall efficiency of an interactive product configuration process. We evaluated Zen-FIX with a real- world case study containing 52454 optimization problems, with which we evaluated seven multi-objective search algorithms. Results show that MoCell significantly outperformed all the others: CellDE, IBEA, NSGA-II, PESA2, Random, SPEA2, for most of the problems, in terms of Efficiency (a combined metric of finding optimal solutions and time performance).\&nbsp;},
	author = {Lu, Hong and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@article {24478,
	title = {Zen-ReqOptimizer: A Search-based Approach for Requirements Assignment Optimization},
	journal = {Empirical Software Engineering},
	volume = {22},
	year = {2016},
	month = {03/2016},
	pages = {175-234},
	publisher = {Springer},
	author = {Li, Yan and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@techreport {25291,
	title = {Zen-CI: Combining Automated Configuration Inference and Incremental Conformance Checking to Support Interactive Configuration of Product Lines},
	number = {2015-05},
	year = {2015},
	publisher = {Simula Research Laboratory},
	author = {Lu, Hong and Yue, Tao and Ali, Shaukat and Zhang, Li}
}
@inproceedings {Simula.simula.2862,
	title = {Automated and Incremental Conformance Checking to Support Interactive Product Configuration},
	journal = {The 25th IEEE International Symposium on Software Reliability Engineering (ISSRE)},
	year = {2014},
	type = {Conference},
	abstract = {In the context of product line engineering (PLE), providing immediate feedback on the correctness of a manual configuration step to users has a practical impact on whether a configuration process with tool support can be successfully adopted in practice. Model-based PLE has brought opportunities to enable automated product configuration and derivation for large-scale systems/software, in which models are used as the abstract specification of commonalities and variabilities of products of a product line. In our previous work, we have proposed a UML-based variability modeling methodology and an interactive configuration process. Based on these work, in this paper, we propose an automated and incremental conformance checking approach to ensure that the manual configuration to each variation point conforms to a set of pre-defined conformance rules specified in OCL. The proposed approach, called Zen-CC is implemented as a component of our product configuration and derivation tool, named as Zen-Configurator. The proposed approach is evaluated with two real-world case studies and results showed that the performance of Zen-CC is significantly better than a baseline algorithm checking all the conformance rules at each configuration step. Moreover, the performance of Zen-CC rarely varies during the configuration process, suggesting that our approach is scalable for configuring products with a large number of configuration points.},
	keywords = {Conference},
	author = {Lu, Hong and Yue, Tao and Ali, Shaukat and Nie, Kunming and Zhang, Li}
}
@article {Simula.simula.2262,
	title = {SAMM: an Architecture Modeling Methodology for Ship Command and Control Systems},
	journal = {Software and Systems Modeling},
	number = {1619-1366},
	year = {2014},
	pages = {1-48},
	publisher = {Springer Berlin Heidelberg},
	abstract = {Ship Command and Control Systems (SCCSs) are composed of large-scale, complex, real-time and software-intensive systems that complete tasks collaboratively. Open architecture has been introduced to design the architecture of SCCSs and has been refined into Functional Architecture (FA) and Technical Architecture (TA) to meet architectural requirements such as adapting fast-speed functional and technical changes. Thereby, specifying the architecture of SCCSs, based on FA and TA, becomes a key issue for stakeholders of the domain. In this paper, we propose an architecture modeling methodology (named as SAMM) for describing the architecture of SCCSs. SAMM is derived by following a systematic and generic framework-modeling Goal, domain-specific Conceptual model, architecture Viewpoint, and architecture description Language (GCVL), which guides domain experts to devise domain-specific architecture modeling methodologies of large-scale software-intensive systems. SAMM contains three viewpoints and 22 models, and a UML/SysML-based architecture description language. An industrial application of SAMM, along with the subsequent application of the derived SAMM architecture model (i.e., a deployed SCCS prototype) was conducted to evaluate SAMM. A questionnaire-based survey was also conducted to subjectively evaluate whether SAMM meets the modeling goals and its applicability. Results show that SAMM meets all modeling goals and is easy to apply.},
	author = {Fan, Zhiqiang and Yue, Tao and Zhang, Li}
}
@inproceedings {Simula.simula.2016,
	title = {Constraints: the Core of Supporting Automated Product Configuration of Cyber-Physical Systems},
	journal = {ACM/IEEE 16th International Conference on Model Driven Engineering Languages and Systems},
	year = {2013},
	publisher = {ACM/IEEE},
	type = {Conference},
	address = {.},
	abstract = {In the context of product line engineering of cyber-physical systems, there exists a large number of constraints to support for example consistency checking of design decisions made in hardware and software components during configuration. Manual configuration is not feasible in this context considering that managing and manipulating all these constraints in a real industrial context is way too complicated. Therefore, automation is the only way to address such a complexity. However, to this end, relevant constraints have to be well-specified and characterized in the way such that automated configuration can be enabled. Such automation includes automated configuration value inference, optimizing configuration steps and consistency checking. In this paper, we classify and characterize constraints that are required to be specified to support most of the key functionalities of any automated product configuration solution, based on our experience of studying three industrial product line families.},
	keywords = {Conference},
	author = {Nie, Kunming and Yue, Tao and Ali, Shaukat and Zhang, Li and Fan, Zhiqiang},
	editor = {Grey, Jeff}
}
@inproceedings {Simula.simula.2260,
	title = {A Generic Framework for Deriving Architecture Modeling Methods for Large-Scale Software-Intensive Systems},
	journal = {SAC {\textquoteright}13 Proceedings of the 28th Annual ACM Symposium on Applied Computing},
	year = {2013},
	pages = {1750-1757},
	publisher = {ACM New York},
	type = {Conference},
	address = {New York, NY, USA},
	abstract = {Architecture description is critical for the development of large-scale software-intensive systems. However, there isn\&$\#$39;t a generic framework that can guide modelers through the derivation process of a domain-specific architecture modeling method (AMM). This process is usually complicated especially when the system is large-scale. In this paper, we propose a generic framework (named as GCVL) to guide modelers to devise AMMs for various domains. GCVL framework has been evaluated by applying it to derive an AMM (named as SAMM) for the ship command and control systems. SAMM is evaluated through a full-size industrial application. Results of the evaluation show that GCVL framework is applicable to define AMMs for large-scale software-intensive systems.},
	author = {Fan, Zhiqiang and Yue, Tao and Zhang, Li},
	editor = {Hung, Chih-Cheng and Hong, Jiman}
}
@techreport {25310,
	title = {The Core of Supporting Automated Product Configuration of Cyber-Physical Systems},
	number = {2012-33},
	year = {2012},
	note = {This technical report is an early version of a published conference paper. The published paper can be found here:https://www.simula.no/publications/constraints-core-supporting-automated-product-configuration-cyber-physical-systems},
	publisher = {Simula Research Laboratory},
	author = {Nie, Kunming and Yue, Tao and Ali, Shaukat and Zhang, Li and Fan, Zhiqiang}
}
