Pub Date : 2010-01-19DOI: 10.7551/mitpress/11740.003.0007
筑波技術大学附属図書館
Barnard's computer science community is growing. The number of Computer Science majors at Barnard has doubled over the last several years. Barnard’s Computer Science program offers meaningful computing education and experiences to all Barnard students and partners with Columbia's Computer Science department to offer a major in Computer Science. The program aims to expand students' use and understanding of computation and data analysis across disciplines; offer students opportunities to think critically about the social implications of technology, including how to harness it for social good; promote curricular and pedagogical advances in computer science and its multidisciplinary applications; and provide new models for engaging students and enhancing diversity in computing.
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Pub Date : 2005-03-07DOI: 10.7551/mitpress/11740.003.0012
Apoorva D. Patel
The purpose of life is to obtain knowledge, use it to live with as much satisfaction as possible, and pass it on with improvements and modifications to the next generation.'' This may sound philosophical, and the interpretation of words may be subjective, yet it is fairly clear that this is what all living organisms--from bacteria to human beings--do in their life time. Indeed, this can be adopted as the information theoretic definition of life. Over billions of years, biological evolution has experimented with a wide range of physical systems for acquiring, processing and communicating information. We are now in a position to make the principles behind these systems mathematically precise, and then extend them as far as laws of physics permit. Therein lies the future of computation, of ourselves, and of life.
{"title":"Future Computation","authors":"Apoorva D. Patel","doi":"10.7551/mitpress/11740.003.0012","DOIUrl":"https://doi.org/10.7551/mitpress/11740.003.0012","url":null,"abstract":"The purpose of life is to obtain knowledge, use it to live with as much satisfaction as possible, and pass it on with improvements and modifications to the next generation.'' This may sound philosophical, and the interpretation of words may be subjective, yet it is fairly clear that this is what all living organisms--from bacteria to human beings--do in their life time. Indeed, this can be adopted as the information theoretic definition of life. Over billions of years, biological evolution has experimented with a wide range of physical systems for acquiring, processing and communicating information. We are now in a position to make the principles behind these systems mathematically precise, and then extend them as far as laws of physics permit. Therein lies the future of computation, of ourselves, and of life.","PeriodicalId":383680,"journal":{"name":"Computational Thinking","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121586274","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}
S ystem testing followed by a product release decision are the last guards in assuring software quality—insufficient testing or the wrong release decision can lead directly to the delivery of low-quality software to users. At the same time, relying too much on system testing to guarantee quality is dangerous because it occurs too late to correct poor-quality software. Moreover, previous studies have shown that bug fixing is much costlier during system testing than in earlier phases.1 Therefore, we must not only be aware of factors that increase defects but also seek possible process improvements to reduce defects before system testing. To identify and justify process improvements in individual organizations, where processes, data, and context are varied and unique, we explored using a multivariate modeling technique to analyze past development data collected in organizations. However, unlike some academic approaches, we employed a basic linear regression approach with a limited number of independent variables, each associated with what we call software engineering (SE) beliefs. These are short statements that are attention-getting, understandable, and obviously practically useful, such as “about 80 percent of the defects come from 20 percent of the modules,” or “peer reviews catch 60 percent of the defects.”2 SE beliefs are a kind of practical hypothesis that
{"title":"Software Engineering","authors":"Akito Monden, Masateru Tsunoda, Ken-ichi Matsumoto","doi":"10.7551/mitpress/11740.003.0008","DOIUrl":"https://doi.org/10.7551/mitpress/11740.003.0008","url":null,"abstract":"S ystem testing followed by a product release decision are the last guards in assuring software quality—insufficient testing or the wrong release decision can lead directly to the delivery of low-quality software to users. At the same time, relying too much on system testing to guarantee quality is dangerous because it occurs too late to correct poor-quality software. Moreover, previous studies have shown that bug fixing is much costlier during system testing than in earlier phases.1 Therefore, we must not only be aware of factors that increase defects but also seek possible process improvements to reduce defects before system testing. To identify and justify process improvements in individual organizations, where processes, data, and context are varied and unique, we explored using a multivariate modeling technique to analyze past development data collected in organizations. However, unlike some academic approaches, we employed a basic linear regression approach with a limited number of independent variables, each associated with what we call software engineering (SE) beliefs. These are short statements that are attention-getting, understandable, and obviously practically useful, such as “about 80 percent of the defects come from 20 percent of the modules,” or “peer reviews catch 60 percent of the defects.”2 SE beliefs are a kind of practical hypothesis that","PeriodicalId":383680,"journal":{"name":"Computational Thinking","volume":"16 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":"134286185","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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