Pub Date : 2013-11-11DOI: 10.1109/ASE.2013.6693060
J. Penix
Software Engineering in practice deals with scale in a variety of dimensions. We build large scale systems operating on vast amount of data. We have millions of customers with billions of queries and transactions. We have distributed teams making thousands of changes, running millions of tests and releasing multiple times per day. These dimensions of scale interact to provide challenges for software development tools and processes. The panelists will describe the challenging aspects of scale in their specific problem domains and discuss which software engineering methods work and which leave room for improvement.
{"title":"Big problems in industry (panel)","authors":"J. Penix","doi":"10.1109/ASE.2013.6693060","DOIUrl":"https://doi.org/10.1109/ASE.2013.6693060","url":null,"abstract":"Software Engineering in practice deals with scale in a variety of dimensions. We build large scale systems operating on vast amount of data. We have millions of customers with billions of queries and transactions. We have distributed teams making thousands of changes, running millions of tests and releasing multiple times per day. These dimensions of scale interact to provide challenges for software development tools and processes. The panelists will describe the challenging aspects of scale in their specific problem domains and discuss which software engineering methods work and which leave room for improvement.","PeriodicalId":383960,"journal":{"name":"Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123439510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/ASE.2013.6693059
J. Frank
Mission planning is central to space mission operations, and has benefited from advances in model-based planning software. A model is a description of the objects, actions, constraints and preferences that the planner reasons over to generate plans. Developing, verifying and validating a planning model is, however, a difficult task. Mission planning constraints and preferences arise from many sources, including simulators and engineering specification documents. As mission constraints evolve, planning domain modelers must add and update model constraints efficiently using the available source data, catching errors quickly, and correcting the model. The consequences of erroneous models are very high, especially in the space operations environment. We first describe the space operations environment, particularly the role of the mission planning system. We then describe model-based planning, and briefly review the current state of the practice in designing model-based mission planning tools and the challenges facing model developers. We then describe an Interactive Model Development Environment (IMDE) approach to developing mission planning systems. This approach integrates modeling and simulation environments to reduce model editing time, generate simulations automatically to evaluate plans, and identify modeling errors automatically by evaluating simulation output. The IMDE approach was tested on a small subset of the Lunar Atmosphere and Dust Environment Explorer (LADEE) flight software to demonstrate how to develop the LADEE mission planning system.
{"title":"The challenges of verification and validation of automated planning systems (keynote)","authors":"J. Frank","doi":"10.1109/ASE.2013.6693059","DOIUrl":"https://doi.org/10.1109/ASE.2013.6693059","url":null,"abstract":"Mission planning is central to space mission operations, and has benefited from advances in model-based planning software. A model is a description of the objects, actions, constraints and preferences that the planner reasons over to generate plans. Developing, verifying and validating a planning model is, however, a difficult task. Mission planning constraints and preferences arise from many sources, including simulators and engineering specification documents. As mission constraints evolve, planning domain modelers must add and update model constraints efficiently using the available source data, catching errors quickly, and correcting the model. The consequences of erroneous models are very high, especially in the space operations environment. We first describe the space operations environment, particularly the role of the mission planning system. We then describe model-based planning, and briefly review the current state of the practice in designing model-based mission planning tools and the challenges facing model developers. We then describe an Interactive Model Development Environment (IMDE) approach to developing mission planning systems. This approach integrates modeling and simulation environments to reduce model editing time, generate simulations automatically to evaluate plans, and identify modeling errors automatically by evaluating simulation output. The IMDE approach was tested on a small subset of the Lunar Atmosphere and Dust Environment Explorer (LADEE) flight software to demonstrate how to develop the LADEE mission planning system.","PeriodicalId":383960,"journal":{"name":"Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering","volume":"57 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116558091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2013-11-11DOI: 10.1109/ASE.2013.6693058
J. Hellerstein
With the rapid expansion of cloud infrastructure and mobile devices, distributed systems have quickly emerged as a dominant computing platform. Distributed systems bring significant complexity to programming, due to platform issues including asynchrony, concurrency, and partial failure. Meanwhile, scalable distributed infrastructure--notably "NoSQL" systems--have put additional burdens on programmers by sacrificing traditional infrastructure contracts like linearizable or transactional I/O in favor of high availability. A growing segment of the developer community needs to deal with these issues today, and for the most part developers are still using languages and tools designed for sequential computation on tightly coupled architectures. This has led to software that is increasingly hard to test and hard to trust. Over the past 5 years, the BOOM project at Berkeley has focused on making it easier to write correct and maintainable code for distributed systems. Our work has taken a number of forms, including the development of the Bloom programming language for distributed systems, tools for testing and checking distributed programs, and the CALM Theorem, which connects programmer level concerns of determinism to system-level concerns about the need for distributed coordination. This talk will reflect on this work, and highlight opportunities for improved collaboration between the software engineering and distributed systems research communities.
{"title":"BOOM: experiences in language and tool design for distributed systems (keynote)","authors":"J. Hellerstein","doi":"10.1109/ASE.2013.6693058","DOIUrl":"https://doi.org/10.1109/ASE.2013.6693058","url":null,"abstract":"With the rapid expansion of cloud infrastructure and mobile devices, distributed systems have quickly emerged as a dominant computing platform. Distributed systems bring significant complexity to programming, due to platform issues including asynchrony, concurrency, and partial failure. Meanwhile, scalable distributed infrastructure--notably \"NoSQL\" systems--have put additional burdens on programmers by sacrificing traditional infrastructure contracts like linearizable or transactional I/O in favor of high availability. A growing segment of the developer community needs to deal with these issues today, and for the most part developers are still using languages and tools designed for sequential computation on tightly coupled architectures. This has led to software that is increasingly hard to test and hard to trust. Over the past 5 years, the BOOM project at Berkeley has focused on making it easier to write correct and maintainable code for distributed systems. Our work has taken a number of forms, including the development of the Bloom programming language for distributed systems, tools for testing and checking distributed programs, and the CALM Theorem, which connects programmer level concerns of determinism to system-level concerns about the need for distributed coordination. This talk will reflect on this work, and highlight opportunities for improved collaboration between the software engineering and distributed systems research communities.","PeriodicalId":383960,"journal":{"name":"Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering","volume":"155 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134229395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering","authors":"","doi":"10.5555/3107656","DOIUrl":"https://doi.org/10.5555/3107656","url":null,"abstract":"","PeriodicalId":383960,"journal":{"name":"Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130979520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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