University of Toronto Department of Computer Science Lecture 3 What is Engineering What is engineering about Engineering vs Science Devices vs Systems How is software engineering different Engineering as a profession Engineering Projects Project Management Project Initiation Project Lifecycles Software Engineering lifecycles Waterfalls spirals etc Requirements Lifecycles Easterbrook 2004 1 University of Toronto Department of Computer Science Engineering vs Science Traditional View Scientists create knowledge study the world as it is are trained in scientific method use explicit knowledge are thinkers Engineers apply that knowledge seek to change the world are trained in engineering design use tacit knowledge are doers More realistic View Scientists create knowledge are problem driven seek to understand and explain design experiments to test theories prefer abstract knowledge but rely on tacit knowledge Engineers create knowledge are problem driven seek to understand and explain design devices to test theories prefer contingent knowledge but rely on tacit knowledge Both involve a mix of design and discovery Easterbrook 2004 2 University of Toronto Department of Computer Science What is engineering Engineering is the development of cost effective solutions to practical problems through the application of scientific knowledge Cost effective Consideration of design trade offs esp resource usage Minimize negative impacts e g environmental and social cost Solutions Emphasis on building devices Practical problems solving problems that matter to people improving human life in general through technological advance Application of scientific knowledge Systematic application of analytical techniques Easterbrook 2004 3 University of Toronto Department of Computer Science Devices vs Systems Normal design Old problems whose solutions are well known Engineering codifies standard solutions Engineer selects appropriate methods and technologies Design focuses on well understood devices Devices can be studied independent of context Differences between the mathematical model and the reality are minimal Radical design Never been done or past solutions have failed Often involves a very complex problem Bring together complex assemblies of devices into new systems Such systems are not amenable to reductionist theories Such systems are often soft no objective criteria for describing the system Examples Most of Computer Engineering involves normal design All of Systems Engineering involves radical design by definition Much of Software Engineering involves radical design soft systems Easterbrook 2004 4 University of Toronto Department of Computer Science Is software different Software is different software is invisible intangible abstract its purpose is to configure some hardware to do something useful there are no physical laws underlying software behaviour there are no physical constraints on software complexity software never wears out traditional reliability measures don t apply software can be replicated perfectly no manufacturing variability Software Myths Myth Myth Myth Myth Myth Myth Easterbrook 2004 Cost of software is lower than cost of physical devices Software is easy to change Computers are more reliable than physical devices Software can be formally proved to be correct Software reuse increases safety and reliability Computers reduce risk over mechanical systems 5 University of Toronto Department of Computer Science Professional Responsibility ACM IEEE code of ethics PUBLIC act consistently with the public interest CLIENT AND EMPLOYER act in a manner that is in the best interests of your client and employer consistent with the public interest PRODUCT ensure that your products and related modifications meet the highest professional standards possible JUDGEMENT maintain integrity and independence in your professional judgment MANAGEMENT subscribe to and promote an ethical approach to the management of software development and maintenance PROFESSION advance the integrity and reputation of the profession consistent with the public interest COLLEAGUES be fair to and supportive of your colleagues SELF participate in lifelong learning and promote an ethical approach to the practice of the profession Of particular relevance in RE Competence never misrepresent your level of competence Confidentiality respect confidentiality of all stakeholders Intellectual property rights respect protections on ideas and designs Data Protection be aware of relevant laws on handling personal data Easterbrook 2004 6 University of Toronto Department of Computer Science Project Management A manager can control 4 things Resources can get more dollars facilities personnel Time can increase schedule delay milestones etc Product can reduce functionality e g scrub requirements Risk can decide which risks are acceptable To do this a manager needs to keep track of Effort How much effort will be needed How much has been expended Time What is the expected schedule How far are we deviating from it Size How big is the planned system How much have we built Defects How many errors are we making How many are we detecting And how do these errors impact quality Initially a manager needs good estimates and these can only come from a thorough analysis of the problem You Youcannot cannotcontrol controlthat thatwhich whichyou youcannot cannotmeasure measure Easterbrook 2004 7 University of Toronto Department of Computer Science Project Types Reasons for initiating a software development project Problem driven competition crisis Change driven new needs growth change in business or environment Opportunity driven exploit a new technology Legacy driven part of a previous plan unfinished work Relationship with Customer s Customer specific one customer with specific problem May be another company with contractual arrangement May be a division within the same company Market based system to be sold to a general market In some cases the product must generate customers Marketing team may act as substitute customer Community based intended as a general benefit to some community E g open source tools tools for scientific research funder customer if funder has no stake in the outcome Hybrid a mix of the above Easterbrook 2004 8 University of Toronto Department of Computer Science Project Context Existing System There is nearly always an existing system May just be a set of ad hoc workarounds for the problem Studying it is important If we want to avoid
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