Software production settings differ in a number of ways, application, database management, structural analysis, signal processing, refinery process, PC-clone, etc.) etc. The experimental nature of KE... limitations of Our survey obtained 58 participants from all Brazilian regions. produtos de qualidade o mais rápido possível. What affects software productivity and how do we improve it? cost estimation models corroborates the same kind of findings that Mohanty`s study shows. Advertisement. still insufficient objective evidence on business benefits by its deployment. But if, requirements are frequently renegotiated, if senior software engineers quit after the preliminary, architectural design, or if there are no modern software requirements, specification, or design aids. Likewise, that a, (3 of 36) [2/11/02 11:38:38 AM], software development effort with productivity 2X is twice as productive as another effort whose, productivity is X. Software, products, production processes, and production setting characteristics all can be influential but, not necessarily the same time or with the same computing resources. Albrecht [2,3] developed the `function point' measure to compare the productivity in 24 projects, that developed business applications. Suffice to say that a, knowledge organization scheme is essential, and that such a scheme must again accomodate the, kinds software production data outlined in Section 5. This software is becoming more popular and more in demand to learn for employment. Develop productivity data collection instrument (form or questionnaire), 2. More productivity with Nitro Pro 13 Small and medium sized companies buy the right PDF program at, where all products can be easily compared. Keen, P.G.W., `Information Systems and Organizational Change'. Further, they found that, teams shifted from one structure to another for either planned or unplanned reasons. Conclusions indicate that workstation use can be quite favorable on software development projects, but the workstations are still only automating the more mundane aspects of the requirements analysis and design phases. Kemerer, C.F., `An Empirical Validation of Software Cost Estimation Models', 31. involve digital devices only, and thus calculate productivity based on software usage duration. Data that is out there and, easy to collect, such as lines of code, does not necessarily tell us anything about how those lines, of code were produced, what tools were used, what problems were encountered, who wrote what, code, etc. However, it is unclear whether the function point technique works equally well on non-business application, systems that do not rely on accessing large files, retrieving selected data, performing some, computations on the data, and producing various reports. This suggests that we need a more robust, theoretical framework, analytical methods, and support tools to address the dilemmas now. and ratio measures. Also, as measured by the frequency, and distribution of changes in the configuration of both the software architecture and the, staff participating, and the amount of existing (or reused) system components incorporated, density of discovered inconsistencies (bugs) found between a module's detailed design and, actually spent on) testing, effort spent to repair detected errors, density of known error, types, and the amount of automated mechanisms employed to generate and evaluate test, Similar variables for consideration can also be articulated for other system development and, evolution activities including quality assurance and configuration management, preliminary, customer (beta-site) testing, customer documentation production, delivery turnover, sustained, In addition, we must also appreciate that software production can be organized into different. Qualitative case studies can provide in-depth, descriptive knowledge about how software production work occurs, what sort of problems arise, and when. ease of rendering or displaying the results of data analysis for different audiences (e.g.. how to handle (or delete) anomalous data collected with survey instruments. These three sections set the stage for Section 4 which provides a discussion of the, measurable variables that appear to affect software productivity. ), (24 of 36) [2/11/02 11:38:38 AM], (how often does a new release of the operating system, processor memory upgrade, new, computing peripherals introduced, etc. conclude that almost no model can estimate the true cost of software with any degree of accuracy. Free Download or Buy PDFelement right now! Vosburg, J., B. Curtis, R. Wolverton, B. Albert, H. Malec, S. Hoben and Y. Liu. Pattern Analysis and Machine Intelligence. Jones does, an effective job at describing some of the problems and paradoxes that plague most software, productivity and quality measures based upon his previous studies [27]. We do not know to what extent individual characteristics, such as personality or programming experience, predict the performance in such tasks. This means that different cost estimation models, and by logical extension, productivity models, lead to differrent measured values which can show great variation when, applied to software development projects. They, surveyed the opinions of hundreds of system users in 10 different user sites. As a result of his, analysis, Jeffrey asserts (a) there is an optimal staff level which depends on the language used and, the size of the resulting software system, and (b) adding staff beyond the optimal point decreases, productivity and increases total development elasped time. em um projeto de software. 51. software productivity are surveyed, and challenges involved in measuring Clearly, developing such theory is a basic research problem, and a problem that must be informed by, systematic empirical examination of current software development projects and practices. Pengelly, A, M. Norris, R. Higham, `Software Process Modelling and Measurement - A, 47. (with large staffs) can mitigate some of this variance. This report examines the current state of the art in software productivity measurement. (13 of 36) [2/11/02 11:38:38 AM], In setting his sights on identifying software productivity improvements opportunities, Boehm, [10] also identifies some of the dilemmas encountered in defining what things need to be, measured to understand software productivity. part by the reasons for measuring productivity noted above. Curtis, B., `Measurement and Experimentation in Software Engineering'. To date, a number of reasons for measuring software productivity have been reported. We developed our theory based on an established taxonomy framework by Gregor (2006). It is easy to use and a productivity tool that you will find very useful especially when you have a lot of tasks that need a collaborative effort to be completed in time. 30. project manager to control them. Multiple, comparative case studies are much less, common and require a greater sustained field research effort. Section 3 provides a select survey of studies that attempt to, identify and measure what affects software productivity. 44. Therefore, we should not limit production measurement attention to only one product, especially, if comparable effort is committed to producing other closely related products. Ultimately, we should articulate an. Key features of such a program are management commitment and an integrated approach', Jeffrey [26] describes a comparative study of software productivity among small teams in 38, development projects in three Austrialian firms. ... Software productivity has been discussed by several studies in software engineering [16,22, ... Outro fator importante é o compromisso do colaborador com o projeto. Such capabilities are not possible with. automated data collection procedures will suffice. Configuration. Software productivity improvement will not come from better, software development technologies alone. However, he does not discuss how his model works, or what, equations it solves. P.K. Given the unit of analysis, should software productivity be examined at the level of, individual programmers, small work groups, software life cycle activities, development, organization, company, or industry? With the PDF creator you can convert... CD-LabelPrint 1.4.2. These, strategies draw attention to the development activities or processes that add value to an emerging, software system. The choice of LSS is motivated by economic and practical, considerations. de Conferências de Decisão, Abordagem Multicritério de Apoio à Decisão, SODA some fundamentals of measurement are described. On the other hand, software testing and integration consume the largest share, usually representing 50% of the, development effort. based technology would enable project managers, developers, or analysts to query a model, conduct `what if' analysis, diagnose project development anomalies, and generate explanations, about how certain project conditions affect productivity. They found six teamwork structures (ie, patterns of interaction) recurring among all the teams in their study. More specific questions such as these can be answered by retrieval from the knowledge base. Background For example, Kling and Scacchi [35] observe at least five, different kinds of rationale are common: respectively, those whose terms emphasize (a) features, of the underlying technology, (b) attributes of the organization setting, (c) improving relations, between software people and management, (d) determining who can affect control over, or, benefit from, a productivity measurement effort (addressing organizational politics), and (e) the, ongoing social interactions and negotiations that characterize software production work. Garg, P.K., P. Mi, T. Pham, W. Scacchi, and G. Thunquest, `The SMART Approach to. using tools and techniques from the domain of software process engineering. It is ideal when there is more than one person working on a design project and they need to keep track of each other's ideas. However, in a review of this and other similar studies, Conte and colleagues [17] report, that average response time is not as critical as a narrow variance in expected response time. In short. The studies, in earlier sections clearly indicate that different LSS, characteristics individually and collectively affect software productivity. So an appropriate strategy is to focus in organizing and managing the project to cultivate, staff commitment to each other and to the project's objectives [cf. characteristics that affect software cost and productivity. incentives, investments in prior technologies and work arrangements, local job markets, occupational and career contingencies, and organizational politics can dramatically affect, software productivity potential, either positively or negatively [6,29,35,50]. contrast to the preceding software productivity studies that employ only univariate analysis. The present work identifies three new factors that influence the software factory, demonstrating that the use of rules and events influences analysis & design, team heterogeneity negatively affects analysis and design and positively affects programming; and the osmotic communication affects programming. Since LSS development efforts can entail thousands or more of such. Programming language in use (Assembly, Fortran, Cobol, C, C++, Ada, CommonLisp, Computing applications (telecommunications switch, command and control, AI research. modo a preencher tal espaço no contexto dos projetos de software, esta dissertação Similarly, each alternative implies certain, kinds of data be collected. Essentially, they argue that response time has an impact on LSS development projects, so that, ample processing resources are critical to enhancing software productivity. Google LLC. Productivity in software factories is very important because it allows organizations to achieve greater efficiency and effectiveness in their activities. Therefore, surveys are best suited for `snap-shot' studies, although multiple or. Free Download or Buy PDFelement right now! Thus, it is unclear what the net change in, software productivity might be if CASE tools that support structured design and programming, (9 of 36) [2/11/02 11:38:38 AM], In a provocative yet systematic comparison of industrial software productivity in the U.S. and, Japan, Cusumano and Kemerer [21] argue that Japanese software development capabilities are, comparable to those found in the U.S. [20].