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LARGE SCALE REQUIREMENTS ANALYSIS AS HETJEROGENEOUS ENGINEERING solution space resides. Each problem is constructed, legitimized, and owned by one or more principals who reside in this ecology. Hence, all problems are ecologically situated (Suchman 1987), socially constructed (Berger and Luckmann 1966; March and Olsen 1976) and owned. Contextualization includes all of these concepts. 15 For the sake of clarity, each anomaly in figure has been defined into a problem. 16 Problem blossoming is similar to an aspect of Checkland’s Soft Systems Methodology (SSM) (Checkland 1981). However, problem blossoming is focused on problem discovery and identification of likely impacted parts of St. SSM, in contrast, focuses on the whole process of systems development. Problem blossoming and SSM share the basic components of iterative learning and discovery, as well as a recognition of the ability to change the current solution and problem spaces as per new insights. 17 This space, S, is similar to Davis’ product space defined as “the range of all possible problem solutions that meets all known constraints.” Software Requirements: Objects, Functions, and States, pp. 42 18 We are using here the concept of frame as defined by Bijker (Bijker 1987). Bijker uses the term frame to denote a new aggregate of concepts and techniques employed by a community of problem-solvers in its problem solving. Changes in frames embody “revolutions” and discontinuities in technology evolution. Such discontinuities are a reflected in such elements as goals of technology use, theories and concepts, problem-solving steps and tools, and organizational and managerial principles related to problem solving practices. An example of this is moving from structured programming to object-oriented programming. 19 In theoretic terms, a solution space is the subset of the Local Solution Space (S) that is affected by as well as affecting the system change. In practical terms, a solution space can be represented as a model of this space, for instance a (richly detailed) workflow or equivalent model. 20 For clarity purposes, t+x is replaced by t+1 throughout the rest of the paper. This is used to indicate the time of an operational future solution space. 21 Loucopoulos & Karakostas, as well as most of the requirements engineering literature, transforms a capability into functionality and a condition into a constraint (e.g., nonfunctionality). Functionality \ represents a want, need or desire of one or more principals in St for a new capability to be made available in response to one or more of their problems in Pt. This corresponds with Gause and Weinberg’s view on requirements engineering: a process to discover what people desire (Gause and Weinberg 1989). It is also in line with Graham’s view, which focuses on what is needed by 23 people (Graham 1998). A constraint represents a condition on a future solution in response to a problem. 22 This same observation has been confirmed by researchers advocating the spiral model of software development which emphasizes the evolution and learning of requirements and the dynamic nature of mappings between requirements and implementations (see e.g. (Boehm 1988; Iivari, 1990a; Iivari 1990b)). 23 As discussed earlier, there could be many arcs from a solution source to a single problem. Still, each arc is an individual requirement. Also, the arrow on the arc (which is informational only) indicates the node pointed to is contextualized by the node pointed from. In this case, a problem is contextualized by a solution. 24 A constraint does not originate in a formal document. It is rooted in one or more problems that are implied in the formal document. Since, by definition, these contextualizing problems come from a formal document, they must have principal representation and, thus, are part of Pt. But, these problems are usually not clearly and specifically identified in most formal documents. This means they cannot be accurately represented within Pt. This is a source of potential requirements failure. The possibility exists of not truly solving the problems that the formal document wanted addressed by the project under consideration even if the constraints (or objectives) specified in a formal document are well met. In turn, this allows for the increased probability of an incorrect future solution, resulting in eventual deployment failure. Altogether, this highlights the importance of constraints as traceable relationships, not just stated restrictions. 25 The lightened and bolded parts of St+1 corresponds to proposed changes in the solution space St in response to the problems in Pt. 26 cf. Bak’s adding a grain of sand causing an avalanche in a sand pile (Bak 1996).
EVOLUTIONARY PROJECT MANAGEMENT: MULTIPLE PERFORMANCE, QUALITY AND COST METRICS FOR EARLY AND CONTINUOUS STAKEHOLDER VALUE DELIVERY - An agile approach Tom Gilb www.Gilb.com Abstract: Agile methods need to include stakeholder metrics in order to ensure that projects focus better on the critical requirements, and that projects are better able to measure their achievements, and to adapt to feedback. This paper presents a short, simple defined process for evolutionary project management (Evo), and discusses its key features. 1 INTRODUCTION In 2001, a British Computer Society Review paper indicated that only 13% of 1027 surveyed IT projects were ‘successful’ (Taylor 2001). In the same year, a Standish report indicated that although there has been some recent improvement, 23% of their surveyed projects were considered total failures and only 28% totally successful (that is, on time, within budget and with all the required functionality) (Johnson 2001: Extracts from Extreme Chaos 2001, a Standish Report). The US Department of Defense, a few years ago, estimated, that about half its software projects failed (Personal Communication, Norm Brown, SPMN (Software Program Managers Network)/Navy). While these figures represent an improvement on the 75% reported for failed DoD projects when the Waterfall Method dominated (Jarzombek 1999), they are still of extreme concern. We must be doing something very wrong. What can senior management and IT project management do about this situation in practice? Some people recommend complex development process standards such as CMM (Capability Maturity Model®), CMMI (Capability Maturity Model® Integration), SPICE (Software Process Improvement and Capability dEtermination) and their like. I am not convinced that these are ‘good medicine’ for even very large systems engineering projects, and certainly they are overly complex for most IT projects. Other people recommend agile methods – these are closer to my heart – but maybe, for non-trivial projects - they are currently ‘too simple’? I. Seruca et al. (eds.), Enterprise Information Systems VI, © 2006 S pringer. Printed in the Netherlands. 24 24– 29. 2 STAKEHOLDER METRICS I believe agile methods would benefit, if they included ‘stakeholder metrics’. I have three main reasons for suggesting this: - to focus on the critical requirements: All projects, even agile projects, need to identify all their stakeholders, and then identify and focus on the ‘top few’ critical stakeholder requirements. - to measure progress: Critical requirements need to be quantified and measurable in practice. Quantified management is a necessary minimum to control all but the smallest upgrade efforts. - to enable response to feed back: By responding to real experience, and modifying plans accordingly, projects can make better progress. This is something that agile projects with their short cycles, can especially utilize. In this paper, I shall present a simple, updated ‘agile’, evolutionary project management process and explain the benefits of a more focused, quantified approach. I recommend the following evolutionary project management process and policy:
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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AN EXPERIENCE IN MANAGEMENT OF IMPR
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ANALYSIS AND RE-ENGINEERING OF WEB
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Module p0 Customer Itinerary Send I
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departments: the marketing dept. se
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• An acyclic module M is usable,
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BALANCING STAKEHOLDER’S PREFERENC
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The scenarios will provide an abstr
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BALANCING STAKEHOLDER'S PREFERENCES
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BALANCING STAKEHOLDER'S PREFERENCES
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A POLYMORPHIC CONTEXT FRAME TO SUPP
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A POLYMORPHIC CONTE XT FRAME TO SUP
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FEATURE MATCHING IN MODEL-BASED SOF
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combination of solution domains - a
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• features for all the concepts:
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Existence In both steps it is possi
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matching strategies are maximal, mi
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TOWARDS A META MODEL FOR DESCRIBING
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elementary message (ae-message) can
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problems on a pragmatic level, whic
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TOWARDS A META MODEL FOR DESCRIBING
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INTRUSION DETECTION SYSTEMS USING A
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INTRUSION DETECTION SYSTEMS USING A
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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CABA 2 L A BLISS PREDICTIVE COMPOSI
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y disables evidencing severe mental
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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