{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:40:44Z","timestamp":1776883244671,"version":"3.51.2"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"DOI":"10.13039\/501100014538","name":"Indonesia Endowment Fund for Education","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014538","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Auton. Adapt. Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>Non-Functional Requirements (NFRs) play a critical role in driving self-adaptation in software systems. In Self-Adaptive Systems (SAS), satisfying multiple NFRs simultaneously introduces significant complexity, as these requirements often conflict\u2014improving one NFR can negatively impact others. Addressing such tradeoffs becomes even more challenging due to the varying degrees of observability of NFRs, with some being fully observable and others only partially observable. Traditional approaches to SAS decision-making, such as those based on Markov Decision Processes (MDPs), often assume homogeneous observability, which limits their ability to address these challenges effectively.<\/jats:p>\n                  <jats:p>We argue that treating NFRs as having mixed observability\u2014where some are fully observable and others are partially observable\u2014enables more effective decision-making. How can SAS model and resolve tradeoffs among NFRs with mixed observability to achieve better outcomes?<\/jats:p>\n                  <jats:p>This article introduces SPECTRA, a multi-objective decision framework based on MDPs. SPECTRA addresses tradeoffs among NFRs by leveraging a multi-objective Mixed Observability Markov Decision Process (MOMDP), which models and handles the varying observability of NFRs effectively. The approach is evaluated using scenarios from MirrorNet, a realistic Remote Data Mirroring (RDM) system utilizing Software-Defined Networking (SDN). Results show that SPECTRA achieves higher utility values, faster policy planning, and more effective tradeoffs compared to existing approaches.<\/jats:p>","DOI":"10.1145\/3735643","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T12:01:31Z","timestamp":1747224091000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["SPECTRA: A Markovian Framework for Managing NFR Tradeoffs in Systems with Mixed Observability"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4679-067X","authenticated-orcid":false,"given":"Hargyo T. N.","family":"Ignatius","sequence":"first","affiliation":[{"name":"University of Birmingham, Birmingham, United Kingdom and Universitas Multimedia Nusantara, Tangerang, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8885-7687","authenticated-orcid":false,"given":"Huma","family":"Samin","sequence":"additional","affiliation":[{"name":"University of Exeter, Exeter, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1139-5795","authenticated-orcid":false,"given":"Rami","family":"Bahsoon","sequence":"additional","affiliation":[{"name":"University of Birmingham, Birmingham, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6895-1636","authenticated-orcid":false,"given":"Nelly","family":"Bencomo","sequence":"additional","affiliation":[{"name":"Durham University, Durham, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2420686"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/IWQOS.2011.5931324"},{"key":"e_1_3_2_4_2","first-page":"394","volume-title":"International Conference on Machine Learning","author":"Nunes Alegre Lucas","year":"2022","unstructured":"Lucas Nunes Alegre, Ana Bazzan, and Bruno C. Da Silva. 2022. Optimistic linear support and successor features as a basis for optimal policy transfer. In International Conference on Machine Learning. PMLR, 394\u2013413."},{"key":"e_1_3_2_5_2","first-page":"239","volume-title":"the AAAI Conference on Artificial Intelligence (AAAI\/IAAI)","author":"Craig Boutilier","year":"2002","unstructured":"Craig Boutilier. 2002. A POMDP formulation of preference elicitation problems. In the AAAI Conference on Artificial Intelligence (AAAI\/IAAI). Edmonton, AB, 239\u2013246."},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1007\/978-3-540-30217-9_73","volume-title":"8th International Conference on Parallel Problem Solving from Nature-PPSN VIII","author":"Branke J\u00fcrgen","year":"2004","unstructured":"J\u00fcrgen Branke, Kalyanmoy Deb, Henning Dierolf, and Matthias Osswald. 2004. Finding knees in multi-objective optimization. In 8th International Conference on Parallel Problem Solving from Nature-PPSN VIII. Springer, 722\u2013731."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scico.2018.07.002"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2010.92"},{"key":"e_1_3_2_9_2","first-page":"108089","volume-title":"Engineering Applications of Artificial Intelligence","author":"de Moraes Rodrigo Saar","year":"2024","unstructured":"Rodrigo Saar de Moraes and Simin Nadjm-Tehrani. 2024. NetGAP: A graph grammar approach for concept design of networked platforms with extra-functional requirements. Engineering Applications of Artificial Intelligence 133 (2024), 108089."},{"key":"e_1_3_2_10_2","first-page":"109","volume-title":"2019 IEEE\/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)","author":"D\u00fcrschmid Tobias","year":"2019","unstructured":"Tobias D\u00fcrschmid, Eunsuk Kang, and David Garlan. 2019. Trade-off-oriented development: making quality attribute trade-offs first-class. In 2019 IEEE\/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER). IEEE, 109\u2013112."},{"key":"e_1_3_2_11_2","volume-title":"the 2011 ACM Symposium on Applied Computing (SAC \u201911)","author":"Elahi Golnaz","year":"2011","unstructured":"Golnaz Elahi and Eric Yu. 2011. Requirements trade-offs analysis in the absence of quantitative measures: A heuristic method. In the 2011 ACM Symposium on Applied Computing (SAC \u201911). ACM, New York, NY."},{"key":"e_1_3_2_12_2","unstructured":"P. Emami A. J. Hamlet and C. Crane. 2015. POMDPy: An Extensible Framework for Implementing Partially-Observable Markov Decision Processes in Python. Retrieved from https:\/\/api.semanticscholar.org\/CorpusID:246821255"},{"key":"e_1_3_2_13_2","article-title":"Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks","author":"Bencomo N.","year":"2013","unstructured":"N. Bencomo, A. Belaggoun, and V. Issarny. 2013. Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks. In 2nd International NSF sponsored Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE \u201913).","journal-title":"2nd International NSF sponsored Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE \u201913)"},{"key":"e_1_3_2_14_2","volume-title":"8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","author":"Bencomo N.","year":"2013","unstructured":"N. Bencomo, A. Belaggoun, and V. Issarny. 2013. Dynamic decision networks to support decision-making for self-adaptive systems. In 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)."},{"key":"e_1_3_2_15_2","first-page":"75","volume-title":"IEEE Transactions on Software Engineering","volume":"42","author":"Filieri A.","year":"2016","unstructured":"A. Filieri, G. Tamburrelli, and C. Ghezzi. 2016. Supporting self-adaptation via quantitative verification and sensitivity analysis at run time. IEEE Transactions on Software Engineering 42 (2016), 75\u201399."},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2593929.2593930"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00766-011-0129-9"},{"key":"e_1_3_2_18_2","volume-title":"the 36th International Conference on Software Engineering (ICSE \u201914)","volume":"12","author":"Letier Emmanuel","year":"2014","unstructured":"Emmanuel Letier, David Stefan, and Earl T. Barr. 2014. Uncertainty, risk, and information value in software requirements and architecture. In the 36th International Conference on Software Engineering (ICSE \u201914), 12 pages."},{"key":"e_1_3_2_19_2","first-page":"147","volume-title":"2016 IEEE International Conference on Autonomic Computing (IEEE ICAC)","author":"Moreno Gabriel A.","year":"2016","unstructured":"Gabriel A. Moreno, Javier C\u00e1mara, David Garlan, and Bradley Schmerl. 2016. Efficient decision-making under uncertainty for proactive self-adaptation. In 2016 IEEE International Conference on Autonomic Computing (IEEE ICAC), 147\u2013156."},{"key":"e_1_3_2_20_2","volume-title":"Conference on Software and Data Technologies","author":"Pedro Sousa Jo\u00e3o","year":"2008","unstructured":"Jo\u00e3o Pedro Sousa, Rajesh Krishna Balan, Vahe Poladian, David Garlan, and Mahadev Satyanarayanan. 2008. User guidance of resource-adaptive systems. In Conference on Software and Data Technologies."},{"key":"e_1_3_2_21_2","volume-title":"MODELS","author":"Song Hui","year":"2013","unstructured":"Hui Song, Stephen Barrett, Aidan Clarke, and Siobh\u00e1n Clarke. 2013. Self-adaptation with end-user preference: Using run-time models and constraint solving. In MODELS."},{"key":"e_1_3_2_22_2","first-page":"9","volume-title":"2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems (RESACS \u201918)","author":"Abdo Ali Saeed Ahmed","year":"2018","unstructured":"Ahmed Abdo Ali Saeed and Seok-Won Lee. 2018. Non-functional requirements trade-off in self-adaptive systems. In 2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems (RESACS \u201918). IEEE, 9\u201315."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3024188"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3643889"},{"key":"e_1_3_2_25_2","first-page":"19","volume-title":"2018 IEEE\/ACM 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)","author":"Paucar Luis Hernan Garcia","year":"2018","unstructured":"Luis Hernan Garcia Paucar and Nelly Bencomo. 2018. RE-STORM: Mapping the decision-making problem and non-functional requirements trade-off to partially observable Markov decision processes. In 2018 IEEE\/ACM 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 19\u201325."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2013.6606549"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36554-0_2"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSN-W50199.2020.00025"},{"key":"e_1_3_2_29_2","unstructured":"IBM. 2023. What Is Observability? Retrieved September 17 2023 from https:\/\/www.ibm.com\/topics\/observability"},{"key":"e_1_3_2_30_2","unstructured":"Hargyo T. N. Ignatius. 2023. XPOMDPy. Retrieved from https:\/\/github.com\/hxi903\/xpomdpy"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/SEAMS51251.2021.00012"},{"key":"e_1_3_2_32_2","volume-title":"2003 USENIX Annual Technical Conference (USENIX ATC \u201903)","author":"Ji Minwen","year":"2003","unstructured":"Minwen Ji, Alistair Veitch, and John Wilkes. 2003. Seneca: Remote mirroring done write. In 2003 USENIX Annual Technical Conference (USENIX ATC \u201903). USENIX Association, San Antonio, TX. Retrieved from https:\/\/www.usenix.org\/conference\/2003-usenix-annual-technical-conference\/seneca-remote-mirroring-done-write"},{"key":"e_1_3_2_33_2","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1016\/S1573-4382(05)80008-6","article-title":"Utility theory with uncertainty","volume":"4","author":"Karni Edi","year":"1991","unstructured":"Edi Karni and David Schmeidler. 1991. Utility theory with uncertainty. Handbook of Mathematical Economics 4 (1991), 1763\u20131831.","journal-title":"Handbook of Mathematical Economics"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2014.2371999"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.5555\/2486788.2487040"},{"key":"e_1_3_2_36_2","unstructured":"Dean Leffingwell and Scaled Agile Team. 2023. Nonfunctional Requirements\u2014Scaled Agile Framework. Retrieved June 22 2023 from https:\/\/scaledagileframework.com\/nonfunctional-requirements"},{"issue":"8","key":"e_1_3_2_37_2","doi-asserted-by":"crossref","first-page":"12724","DOI":"10.1109\/TITS.2021.3117028","article-title":"GaDQN-IDS: A novel self-adaptive IDS for VANETs based on Bayesian game theory and deep reinforcement learning","volume":"23","author":"Liang Junwei","year":"2021","unstructured":"Junwei Liang, Maode Ma, and Xu Tan. 2021. GaDQN-IDS: A novel self-adaptive IDS for VANETs based on Bayesian game theory and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 23, 8 (2021), 12724\u201312737.","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786853"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3149180"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/1982185.1982335"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1177\/0278364910369861"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1109\/SASO.2016.19","volume-title":"2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","author":"Pandey Ashutosh","year":"2016","unstructured":"Ashutosh Pandey, Gabriel A. Moreno, Javier C\u00e1mara, and David Garlan. 2016. Hybrid planning for decision making in self-adaptive systems. In 2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). IEEE, 130\u2013139."},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/SASO.2019.00011","volume-title":"2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)","author":"Paucar Luis Hern\u00e1n Garc\u00eda","year":"2019","unstructured":"Luis Hern\u00e1n Garc\u00eda Paucar and Nelly Bencomo. 2019. Knowledge base K models to support trade-offs for self-adaptation using Markov processes. In 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). IEEE, 11\u201316."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3297280.3299743"},{"key":"e_1_3_2_45_2","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"Puterman Martin L.","year":"2014","unstructured":"Martin L. Puterman. 2014. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2745505"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2017.2727050"},{"key":"e_1_3_2_48_2","first-page":"1666","volume-title":"24th International Joint Conference on Artificial Intelligence (IJCAI), Vol","volume":"2015","author":"Roijers Diederik M.","year":"2015","unstructured":"Diederik M. Roijers, Shimon Whiteson, and Frans A. Oliehoek. 2015. Point-based planning for multi-objective POMDPs. In 24th International Joint Conference on Artificial Intelligence (IJCAI), Vol. 2015. AAAI Press, 1666\u20131672."},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","unstructured":"Eric Rutten Nicolas Marchand and Daniel Simon. 2018. Feedback control as MAPE-K loop in autonomic computing. Software Engineering for Self-Adaptive Systems III. Assurances: International Seminar Dagstuhl Castle Germany December 15\u201319 2013 Revised Selected and Invited Papers 349\u2013373","DOI":"10.1007\/978-3-319-74183-3_12"},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/B978-0-12-802855-1.00011-3","volume-title":"Managing Trade-Offs in Adaptable Software Architectures","author":"Salama Maria","year":"2017","unstructured":"Maria Salama, Rami Bahsoon, and N. Bencomo. 2017. Managing trade-offs in self-adaptive software architectures: A systematic mapping study. In Managing Trade-Offs in Adaptable Software Architectures. Ivan Mistrik, Nour Ali, Rick Kazman, John Grundy, and Bradley Schmerl (Eds.), Elsevier, 249\u2013297."},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1109\/RE51729.2021.00065","volume-title":"2021 IEEE 29th International Requirements Engineering Conference (RE)","author":"Samin Huma","year":"2021","unstructured":"Huma Samin, Nelly Bencomo, and Peter Sawyer. 2021. Pri-AwaRE: Tool support for priority-aware decision-making under uncertainty. In 2021 IEEE 29th International Requirements Engineering Conference (RE). IEEE, 450\u2013451."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10270-021-00956-0"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/SEAMS51251.2021.00039"},{"key":"e_1_3_2_54_2","article-title":"Monte-Carlo planning in large POMDPs. In","author":"Silver David","year":"2010","unstructured":"David Silver and Joel Veness. 2010. Monte-Carlo planning in large POMDPs. In Advances in Neural Information Processing Systems, Vol. 23.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.21.5.1071"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/2001576.2001674"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.5555\/1622519.1622525"},{"key":"e_1_3_2_58_2","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1109\/RE54965.2022.00022","volume-title":"2022 IEEE 30th International Requirements Engineering Conference (RE)","author":"Sutcliffe Alistair","year":"2022","unstructured":"Alistair Sutcliffe, Pete Sawyer, and Nelly Bencomo. 2022. The implications of \u2018soft\u2019 requirements. In 2022 IEEE 30th International Requirements Engineering Conference (RE). IEEE, 178\u2013188."},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1070.0838"},{"key":"e_1_3_2_60_2","volume-title":"the AAAI Conference on Artificial Intelligence","volume":"31","author":"Walraven Erwin","year":"2017","unstructured":"Erwin Walraven and Matthijs Spaan. 2017. Accelerated vector pruning for optimal POMDP solvers. In the AAAI Conference on Artificial Intelligence, Vol. 31."},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.Congress.2013.38"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2021.3066330"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/RE.2009.36"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.5555\/2543993"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.4018\/jwsr.2011040103"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.5555\/3176764.3176770"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1057\/jors.2011.33"}],"container-title":["ACM Transactions on Autonomous and Adaptive Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3735643","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T07:50:55Z","timestamp":1776844255000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3735643"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,22]]},"references-count":66,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6,30]]}},"alternative-id":["10.1145\/3735643"],"URL":"https:\/\/doi.org\/10.1145\/3735643","relation":{},"ISSN":["1556-4665","1556-4703"],"issn-type":[{"value":"1556-4665","type":"print"},{"value":"1556-4703","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,22]]},"assertion":[{"value":"2023-09-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}