Identifying Compiler and Optimization Options from Binary Code using Deep Learning Approaches

D. Pizzolotto and K. Inoue, "Identifying Compiler and Optimization Options from Binary Code using Deep Learning Approaches," 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, 2020, pp. 232-242, doi: 10.1109/ICSME46990.2020.00031

Addressing the evolution of automated user behaviour patterns by runtime model interpretation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10270-013-0371-3The use of high-level abstraction models can facilitate and improve not only system development but also runtime system evolution. This is the idea of this work, in which behavioural models created at design time are also used at runtime to evolve system behaviour. These behavioural models describe the routine tasks that users want to be automated by the system. However, users¿ needs may change after system deployment, and the routine tasks automated by the system must evolve to adapt to these changes. To facilitate this evolution, the automation of the specified routine tasks is achieved by directly interpreting the models at runtime. This turns models into the primary means to understand and interact with the system behaviour associated with the routine tasks as well as to execute and modify it. Thus, we provide tools to allow the adaptation of this behaviour by modifying the models at runtime. This means that the system behaviour evolution is performed by using high-level abstractions and avoiding the costs and risks associated with shutting down and restarting the system.This work has been developed with the support of MICINN, under the project EVERYWARE TIN2010-18011, and the support of the Christian Doppler Forschungsgesellschaft and the BMWFJ, Austria.Serral Asensio, E.; Valderas Aranda, PJ.; Pelechano Ferragud, V. (2013). Addressing the evolution of automated user behaviour patterns by runtime model interpretation. Software and Systems Modeling. https://doi.org/10.1007/s10270-013-0371-3SWeiser, M.: The computer of the 21st century. Sci. Am. 265, 66–75 (1991)Serral, E., Valderas, P., Pelechano, V.: Context-adaptive coordination of pervasive services by interpreting models during runtime. Comput. J. 56(1), 87–114 (2013)Ajila, S.A., Alam, S.: Using a formal language constructs for software model evolution. In: Third IEEE International Conference on Semantic Computing (IEEE-ICSC 2009). Berkeley, CA, USA, pp. 390–395 (2009)Bennett, K., Rajlich, V.: Software Maintenance and Evolution: A Roadmap. In: 22nd International Conference on Software Engineering (ICSE 2000). Limerick, Ireland, pp. 75–87 (2000)Mens, T.: The ERCIM working group on software evolution: the past and the future. In: Proceedings of the Joint International and Annual ERCIM Workshops on Principles of Software Evolution (IWPSE) and Software Evolution (Evol) Workshops. ACM (2009)Mens, T., Wermelinger, M., Ducasse, S., Demeyer, S., Hirschfeld, R.: Challenges in software evolution. In: Report of the ChaSE 2005 Workshop Organised by the ERCIM Working Group on Software Evolution. IWPSE-05. Lisbon, Portugal, pp. 13–22 (2005)Hirschfeld, R., Kawamura, K., Berndt, H.: Dynamic service adaptation for runtime system extensions. In: Wireless On-Demand Network Systems, pp. 227–240. Springer, Berlin, Heidelberg, Madonna di Campiglio, Italy (2004)Lientz, B.P., Swanson, E.B.: Software maintenance management: a study of the maintenance of computer applications software in 487 data processing organizations. Addison-Wesley, Reading, MA (1980)Buckley, J., Mens, T., Zenger, M., Rashid, A., Kniesel, G.: Towards a taxonomy of software change. J. Softw. Maint. Evolut. Res. Pract. 17(5), 309–332 (2003)Hardian, B., Indulska, J., Henricksen, K.: Balancing autonomy and user control in context-aware systems—a survey. In: CoMoRea, IEEE PerCom Workshops 2006. (2006)Biegel, G., Cahill, V.: A framework for developing mobile, context-aware applications. In: The 2nd IEEE Conference on Pervasive Computing and Communication (PerCom), pp. 361–365 (2004)Hofer, T., Schwinger, W., Pichler, M., Leonhartsberger, G., Altmann, J.: Context-awareness on mobile devices—the hydrogen approach. In: The 36th Annual Hawaii International Conference on System Sciences, pp. 292–302 (2002)Dey, A.K.: Understanding and using context. Pers. Ubiquitous Comput. 5(1), 4–7 (2001)Sheng, Q.Z., Benatallah, B.: ContextUML: a UML-based modelling language for model-driven development of context-aware web services. In: Proceedings of the International Conference on Mobile, Business (ICMB’05). pp. 206–212 (2005)Henricksen, K., Indulska, J.: A software engineering framework for context-aware pervasive computing. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications (PerCom 2004), pp. 77–86. IEEE, Orlando, FL, USA (2004)Baldauf, M., Dustdar, S., Rosenberg, F.: A survey on context-aware systems. Int. J. Ad Hoc Ubiquitous Comput. 2(4), 263–277 (2007)Ye, J., Coyle, L., Dobson, S., Nixon, P.: Ontology-based models in pervasive computing systems. Knowl. Eng. Rev. 22(4), 315–347 (2007)Chen, H., Finin, T., Joshi, A.: An ontology for context-aware pervasive computing environments. Special Issue on Ontologies for Distributed Systems. Knowl. Eng. Rev. 18(3), 197–207 (2004)Welty, C., McGuinness, D.L.: OWL Web Ontology Language Guide. vol. W3C Recomm. W3C Recommendation 10 Feb 2004 (2004)Shepherd, A.: HTA as a framework for task analysis. Ergonomics 41, 1537–1552 (1998)Serral, E., Valderas, P., Pelechano, V.: Towards the model driven development of context-aware pervasive systems. Special Issue on Context Modelling, Reasoning and Management. PMC 6(2), 254–280 (2010)Serral, E.: Automating Routine Tasks in Smart Environments. A Context-aware Model-driven Approach, Technical University of Valencia (2011)Mellor, S.J., Balcer, M.J.: Executable UML: A Foundation for Model Driven Architecture. Addison-Wesley, Indianapolis (2002)Muñoz, J., Ferragud, D.V.P.: Model Driven Development of Pervasive Systems. Building a Software Factory. Universidad Politécnica de Valencia, Valencia (2008)Juric, M.B., Sarang, P.: Business Process Execution Language for Web Services: BPEL and BPEL4WS (2006)Loke, S.W., Smanchat, S., Ling, S., Indrawan, M.: Formal mirror models: an approach to just-in-time reasoning for device ecologies. Int. J. Smart Home 2(1), 15–32 (2008)Code Generation conference. http://www.codegeneration.net/cg2010/ (2010)Guy, M.: Report 2: API Good Practice Good practice for provision of and consuming APIs. UKOLN (2009)Bloch, J.: How to design a good API and why it matters. pp. 506–507 (2005)Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. J. Web Semant. 5(2), 51–53 (2007)Bernstein, P.: Multiversion concurrency control—theory and algorithms. ACM Trans. Database Syst. 8(4), 465–484 (1983)Cooper, S., Dann, W., Pausch, R.: Alice: a 3-D tool for introductory programming concepts. J. Comput. Sci. Coll. 15, 107–116 (2000)Pérez, F., Valderas, P.: Allowing end-users to actively participate within the elicitation of pervasive system requirements through immediate visualization. In: Fourth International Workshop on Requirements Engineering Visualization (REV), pp. 31–40. IEEE, Atlanta, Georgia, USA (2009)Lieberman, H., Paternó, F., Wulf, V.: End User Development. Springer, Dordrecht (2006)Nielsen, J.: Usability Engineering. Morgan Kaufmann Publishers Inc, San Francisco (1993)Van Welie, M., Trætteberg, H.: Interaction Patterns in User, Interfaces. pp. 13–16 (2000)Galitz, W.O.: The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and Techniques. Wiley, New York (2002)Kitchenham, B., Pickard, L., Pfleeger, S.L.: Case studies for method and tool evaluation. Softw. IEEE 12(4), 52–62 (1995)Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in Software Engineering. Springer, Berlin (2012)Jones, J.V.: Applied software measurement: assuring productivity & quality (2nd ed’97). McGraw-Hill, New York (1997)Strang, T., Linnhoff-Popien, C.: A context modeling survey. In: First International Workshop on Advanced Context Modelling, Reasoning And Management at UbiComp (2004)Lewis, J.R.: Psychometric Evaluation of an After-Scenario Questionnaire for Computer Usability Studies? The ASQ. SIGCHI Bulletin (1991)Cook, D.J., Youngblood, M., Heierman, I.I.I.E.O., Gopalratnam, K., Rao, S., Litvin, A., Khawaja, F.: MavHome: An Agent-based Smart Home. In: First IEEE International Conference on Pervasive Computing and, Communications (PerCom’03), pp. 521–524 (2003)Hagras, H., Callaghan, V., Colley, M., Clarke, G., Pounds-Cornish, A., Duman, H.: Creating an ambient-intelligence environment using embedded agents. IEEE Intell. Syst. 19(6), 12–20 (2004)Rashidi, P., Cook, D.J.: Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans. Syst. Man Cybern. 39(5), 949–959 (2009)Webb, G.I., Pazzani, M.J., Billsus, D.: Machine learning for user modeling. User model. User-Adapt Interact. 11(1–2), 19–29 (2001)Valiant, L.G.: A theory of the learnable. Commun. ACM 27(11), 1134–1142 (1984)Serral, E., Valderas, P., Pelechano, V.: (2011) Improving the cold-start problem in user task automation by using models at runtime. In: Information Systems Development, pp. 671–683. (2011)García-Herranz, M., Haya, P.A., Esquivel, A., Montoro, G., Alamán, X.: Easing the smart home: semi-automatic adaptation in perceptive environments. J. Univers. Comput. Sci. 14(9), 1529–1544 (2008)Henricksen, K., Indulska, J., Rakotonirainy, A.: Using context and preferences to implement self-adapting pervasive computing applications. Sofw. Pract. Exp. 36(11–12), 1307–1330 (2006)Johnson, P.: Tasks and situations: considerations for models and design principles in human computer interaction, pp. 1199–1204. HCI International. Munich, Germany (1999)Cook, D.J., Das, S.K.: Smart environments: technologies, protocols, and applications, vol. 43. Wiley-Interscience, New York (2005)Paternò, F.: ConcurTaskTrees: an Engineered approach to model-based design of interactive systems. In: The Handbook of Analysis for Human-Computer Interaction, pp. 483–500 (2002)Pribeanu, C., Limbourg, Q., Vanderdonckt1, J.: Task modelling for context-sensitive user interfaces. In: Interactive Systems: Design, Specification, and Verification (DSV-IS), pp. 49–68. Springer, Berlin, Heidelberg 2001, Glasgow, Scotland, UK (2001)Souchon, N., Limbourg, Q., Vanderdonckt., J.: Task modelling in multiple contexts of use. In: Interactive Systems: Design, Specification, and Verification (DSV-IS), pp. 59–73 (2002)Huang, R., Cao, Q., Zhou, J., Sun, D., Su, Q.: Context-aware active task discovery for pervasive computing. In: International Conference on Computer Science and Software Engineering, pp. 463–466. IEEE, Wuhan, China (2008)Sousa, J.P., Poladian, V., Garlan, D., Schmerl, B.: Task-based adaptation for ubiquitous computing. IEEE Trans. Syst. Man Cybern. 36(3), 328–340 (2006)Masuoka, R., Parsia, B., Labrou, Y.: Task Computing—The Semantic Web Meets Pervasive Computing. In: 2nd International Semantic Web Conference on the Semantic Web (ISWC 2003), pp. 866–881. vol. LNCS 2870. Sanibel Island, FL, USA (2003)Oreizy, P., Gorlick, M.M., Taylor, R.N., Heimbigner, D., Johnson, G., Medvidovic, N., Quilici, A., Rosenblum, D.S., Wolf, A.L.: An architecture-based approach to self-adaptive software. IEEE Intell. Syst. Their Appl. 14(3), 54–62 (1999)Floch, J., Hallsteinsen, S., Stav, E., Eliassen, F., Lund, K., Gjørven, E.: Using Architecture Models for Runtime Adaptability. IEEE Software. 23(2), 62–70 (2006)Morin, B., Jézéquel, J.-M., Fleurey, F., Solberg, A.: Models at runtime to support dynamic adaptation. IEEE Comput. Soc. pp. 46–53 (2009)Cetina, C., Giner, P., Fons, J., Pelechano, V.: Using feature models for developing self-configuring smart homes. In: Fifth International Conference on Autonomic and Autonomous Systems, pp. 179–188. IEEE, Valencia, Spain (2009)Garlan, D., Schmerl, B.: Using architectural models at runtime: research challenges. In: Proceedings of the European Workshop on Software Architectures, pp. 200–205. Springer, Berlin, Heidelberg, St Andrews, UK (2004)Blumendorf, M., Lehmann, G., Feuerstack, S., Albayrak, S.: Executable models for human-computer interaction. In: Interactive Systems, Design, Specification, and Verification Workshop (DSV-IS 2008), pp. 238–251. Springer Berlin Heidelberg, Kingston, Canada (2008)Ballagny, C., Hameurlain, N., Barbier, F.: MOCAS: a state-based component model for self-adaptation. In: Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems, pp. 206–215. IEEE, San Francisco, California (2009)Amoui, M., Derakhshanmanesh, M., Ebert, J., Tahvildari, L.: Achieving dynamic adaptation via management and interpretation of runtime models. J. Syst. Softw. 85(12), 2720–2737 (2012)Blair, G., Bencomo, N., France, R.B.: [email protected]. IEEE Comput. 42, 22–27 (2009)Zhang, J., Cheng, B.H.C.: Model based development of dynamically adaptive software. In: International Conference on Software Engineering (ICSE’06), pp. 371–380. ACM, Shanghai, China (2006

Analyzing the Evolution and Maintenance of ML Models on Hugging Face

Hugging Face (HF) has established itself as a crucial platform for the development and sharing of machine learning (ML) models. This repository mining study, which delves into more than 380,000 models using data gathered via the HF Hub API, aims to explore the community engagement, evolution, and maintenance around models hosted on HF, aspects that have yet to be comprehensively explored in the literature. We first examine the overall growth and popularity of HF, uncovering trends in ML domains, framework usage, authors grouping and the evolution of tags and datasets used. Through text analysis of model card descriptions, we also seek to identify prevalent themes and insights within the developer community. Our investigation further extends to the maintenance aspects of models, where we evaluate the maintenance status of ML models, classify commit messages into various categories (corrective, perfective, and adaptive), analyze the evolution across development stages of commits metrics and introduce a new classification system that estimates the maintenance status of models based on multiple attributes. This study aims to provide valuable insights about ML model maintenance and evolution that could inform future model development strategies on platforms like HF.Comment: Accepted at the 2024 IEEE/ACM 21th International Conference on Mining Software Repositories (MSR

COBOL to Java and Newspapers Still Get Delivered

This paper is an experience report on migrating an American newspaper company's business-critical IBM mainframe application to Linux servers by automatically translating the application's source code from COBOL to Java and converting the mainframe data store from VSAM KSDS files to an Oracle relational database. The mainframe application had supported daily home delivery of the newspaper since 1979. It was in need of modernization in order to increase interoperability and enable future convergence with newer enterprise systems as well as to reduce operating costs. Testing the modernized application proved to be the most vexing area of work. This paper explains the process that was employed to test functional equivalence between the legacy and modernized applications, the main testing challenges, and lessons learned after having operated and maintained the modernized application in production over the last eight months. The goal of delivering a functionally equivalent system was achieved, but problems remained to be solved related to new feature development, business domain knowledge transfer, and recruiting new software engineers to work on the modernized application.Comment: 4 pages, Accepted to be Published in: Proceedings of the 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME), September 23-29, 2018, Madrid, Spai

Towards Shaping the Software Lifecycle with Methods and Practices

As software projects are very diverse, each software development process must be adjusted to the needs of the project and the corresponding development team. Frequently, we find different methods and practices combined in a so-called hybrid development method. Research has shown that these hybrid methods evolve over time and are devised based on experience. However, when devising a hybrid method, the methods and practices used should cover the whole software project with its different phases including, among others, project management, requirements analysis, quality management, risk management, and implementation. In this paper, we analyze which methods and practices are used in which phase of a software project. Based on an initial survey with 27 practitioners, we provide a mapping of methods and practices to different project phases and vice versa. Despite the preliminary nature of our study and the small sample size, we observe three remarkable aspects: (1) there are discrepancies between the intended use of methods and practices according to literature and the real use in practice, (2) practices are used more consistently than methods, and (3) parts of the software lifecycle such as maintenance and evolution are hardly covered by widely distributed methods and practices. Consequently, when devising a development process, it is worth a thought whether all phases of the software lifecycle are addressed or not.Comment: Accepted for publication at the Joint 15th International Conference on Software and System Processes (ICSSP) and 16th ACM/IEEE International Conference on Global Software Engineering (ICGSE