Academic Open Internet Journal

ISSN 1311-4360

Volume 23, 2008



Measuring Quality Attributes of Web-based Applications

Part-I: Assessment and Design

Sanjeev Dhawan1, Rakesh Kumar2


1Faculty of Computer Science & Engineering,

University Institute of Engineering & Technology (U.I.E.T), Kurukshetra University, Kurukshetra (K.U.K)- 136 119, Haryana, India



2Faculty of Computer Science,

Department of Computer Science and Applications (D.C.S.A),

Kurukshetra University, Kurukshetra (K.U.K)- 136 119, Haryana, India




Abstract: This paper has been designed to predict the Web metrics for evaluating the reliability and maintainability of hyperdocuments. In the age of Information and Communication Technology (ICT), Web and the Internet, have brought significant changes in Information Technology (IT) and their related scenarios. Therefore in this paper an attempt has been made to trace out the Web-based measurements towards the creation of efficient Web centric applications. The dramatic increase in Web site development and their relative usage has led to the need for predicting Web-based metrics. These metrics will accurately assess the effort in a Web-based application. Here we carried out an empirical study with the students of an advanced university class and Web designers that use various client-server based Web technologies to design Web-based applications for predicting the hypermedia design model. Our first goal was to compare the relative importance of each design activity. Second, we tried to assess the accuracy of a priori design effort predictions and the influence of some factors on the effort needed for each design activity. Third, we also studied the quality of the designs obtained based on construction of a User Behavior Model Graph (UBMH) to capture the dynamics involved in user behavior, which is discussed in part-II of this paper. The results obtained from the assessment can help us to systematically identify the effort assessment and failure points in Web systems and makes the evaluation of reliability of these systems simple. The theoretical aspects and designing have been described in part-I. Part-II describes the variation of the effort estimation with the help of analysis and implementation models of Web-based applications.


Keywords: Empirical software engineering, Web-based effort estimation, Web-based design, Web metrics.


1.   Introduction

Web-based application is essentially a client-server system, which combines traditional effort logic and functionality, usually server based, with the hypermedia navigation and data entry facilities provided by browser technology running on the client. Through a client Web browser, users are able to perform business operations and then to change the state of business data on the server. The range of Web-based applications varies enormously, from simple Web sites (Static Web sites) that are essentially hypertext document presentation applications, to sophisticated high volume e-commerce applications often involving supply, ordering, payment, tracking and delivery of goods or the provision of services (i.e. Dynamic and Active Web sites). We have focused on the implementation and comparison of effort measurement models for Web-based hypermedia applications based on implementation phase of development life cycle. For this work, we studied various size measures at different points in the development life cycle of Web-based systems, to estimate effort, and these have been compared based on several predictions. The main objective of design metrics is to provide basic feedback of the design being measured. There may be general lack of objects, polymorphism, inheritance, high level of coupling between the object classes or an uneven behaviour amongst the classes. The major aim of this paper is to demonstrate an approach, which appears promising in terms of producing a direct assessment of Web-based metrics results. Web-based systems consist of the Web objects (i.e. Web documents, Images, Pictures, Sounds, Scripts, Active X Control). But developing good Web-based application is expensive, mostly in terms of time and degree of difficulty for the Web designers. In the present scenario, the companies developing Web-based systems face the problems and challenges of estimating the required development effort in a fixed time frame. This problem does not have a standard solution yet. On the other hand, effort estimation models that have been used for many years in traditional software development are not very accurate for Web-based software development effort estimation [1]. Moreover, the rapid evolution and growth of Web related technology, tools and methodologies makes historical information and effort estimation models of software engineering quickly obsolete. Like any other software process, Web application development would benefit from early stage effort estimation. Keeping the above-mentioned requirements in mind, we have been engaged in a number of activities involving study of Web-based systems by using practical and theoretical analysis. Efforts have been made to understand the design specifications, and develop the corresponding Web-based modules by using client-server technologies of Web design [2] [3] [4] [5] [6]. The modules have been designed to work in client-server and stand-alone modes under different environment.


2.   Literature review

Literature shows that over the years several techniques for estimating development effort have been suggested in the software development life cycle, and have been compared based on several predictions. Over the years, measurement of the quality of software products has been fraught with difficulties. The first is related to the nature of software, which is a complex intellectual artifact developed through successive iterations and phases until deployed and used in a variety of organizational and industrial contexts. A second difficulty arises from the variety of quality models (such as the ones proposed by Boehm, McCall and Dromey [7-9]), which have not received subsequent continuous investment to generate the corresponding measurement instruments. A third difficulty is the lack of tools in the software engineering community to represent quantitatively viewpoints, while at the same time keeping track of the value of individual views on quality. Finally, there is a fourth difficulty, which is related to the largely paper-based measurement process. This is found by the software engineers working in highly tool-based environments, given that these engineers are culturally declined to such paper-based processes. In literature there are only a few examples of effort estimation models for Web-based development, as most work proposes methods and tools as a basis for process improvement and higher product quality. Several development effort estimation methods have been proposed from the beginning of software engineering as a research area. These methods are classified as traditional software versus Web-oriented software. The traditional effort estimation methods are those used to estimate the development effort of software that consists of programs in a programming language, which eventually interact with data files stored in the database. On the other hand, the Web-based methods use different metrics and they are focused on estimating the development effort of products. These products generally involve code in a Web-based client server technologies, imagery, look-and-feel, information structure, navigation and multimedia objects. Traditional effort estimation methods like COCOMO [10] are mainly based on metrics like Lines Of Code (LOC) [11] or Function Points (FP) [12]. The estimations supported by LOCs have shown several problems. Most working Web projects agree that LOCs are not suitable for early estimation because they are based on design [13]. Other reported problem is that the work involved in the development of multimedia objects and look-and-feel cannot be measured in LOCs. Also, an important amount of reliable historical information is needed to estimate effort using this metric, which reduces the capability to get reliable fast estimations. Speed is an important requisite for Web-based applications. Similarly, traditional estimation methods based on FPs are not appropriate because FPs don’t consider the imagery, navigation design, look-and-feel, and multimedia objects, among others. Then, Boehm proposed COCOMO II, which could use alternatively LOCs, FPs or Object Points [14]. Although COCOMO II was not defined to support the development effort estimation of Web applications, many people found the way to adapt the object point concept in order to get a sizing estimation [15]. The Object Points and COCOMO II seem to be acceptable for traditional software projects, but they are not good enough to get accurate effort estimations for Web-based information systems. The complexity of the estimation process and need for historical information make them difficult to apply in the Web-based applications. Several size metrics has been proposed for Web-based applications, like Object Points, Application Points and Multimedia Points [16]. However, the most appropriate seems to be the Halstead’s equation [12], and it is used to calculate the volume or size of Web Objects in term of operands and operators. The combination of COCOMO II and Web Objects is, in this moment, the most appropriate method to estimate the development effort of Web applications. However, this combination is not feasible to estimate the development effort of small to large size projects, which require fast estimation with little historical information.


2.1   Web-based measurements- in perspective

By now, a very little research work has been carried out in the area of effort estimations and assessment techniques for Web systems. Software effort estimation and assessment is crucial for techniques for high volume Web systems based hypermedia applications, where failures can result in loss of revenue and dissatisfied customers. Web systems based hypermedia applications have led to the emergence of new e-commerce models, which mandate a very high reliability and availability requirements. Companies developing Web-based systems face the challenge of estimating the required development effort in a very short time frame. This problem does not have a standard solution yet. On the other hand, effort estimation models that have been used for many years in traditional software development are not very accurate for Web-based software development effort estimation [1]. Web-based projects are naturally short and intensive [17], so not having an appropriate effort estimation model pushes developers to make highly risky estimations. Moreover, the rapid evolution and growth of Web related technology, tools and methodologies makes historical information quickly obsolete. Nelson et al [18] compute the reliability number by using the ratio of the number of page errors, to the total number of hits to the page for a test case. The computation is based on a static representation of the web pages and does not consider users behavior. There are some commercial tools such as LogExpert, Analog [19] that gives the statistics like page time, pages accessed, referrer details, etc. but does not give comprehensive session level information such as session length, mix of sessions, session count, probability of navigation from one page to another etc. Thus, they do not report the dynamic aspects of user’s navigation. Wang et al [20] have proposed the construction of tree-view of the user’s navigational flow by considering Web server log files as input, and use referrer-id field to derive the result, which represents a dynamic form of input domain model [21]. Here, the construction of the tree-view is calculated through depth-first traversal algorithm taking log files as input. The model represents a system where users avoid re-traversal by remembering the pages traversed before. Menasce et al [22] have proposed a state transition graph to capture the behavior of users for Web workload characterization. Technique proposed by Sengupta [23] considers the navigational flow as input, and uses client-id field in the access log to identify the unique sessions, probabilities associated with occurrence of each session, and the page-level transition probabilities in the session. The reliability is computed by failure data analysis using metrics such as the Mean Time between Failures (MTBF), Mean Time to Fail (MTTF) and Reliability number [24]. In Web systems, the data for failure analysis has primarily been captured from the access logs that have HTTP returns error code of 4xx and 5xx only for the valid sessions.


3.    Development of Web-based application

The unique nature of Web-based applications broadens the role of traditional project management and adds a new dimension to the software development process. Web-based applications often contain significant multimedia content (images, movie clips, sound clips and text) requiring specialist resources for their development. In Web-based application development the participation and contribution of Web designers, programmers, analysts, managers, domain experts etc. plays an important role. For the purposes of estimating software development effort, multimedia content is assumed to exist and the effort required for their production is outside the scope of the software engineering process. However, the effort of integrating these elements needs to taken into account. The development of web-based applications includes the various Web-based client-server technologies: presentation (e.g. HTML, DHTML, XML); scripting and programming languages (e.g. PHP, Coldfusion, VBScript, JavaScript, PERL ActiveX, Servlets, Java, AJAX, Python); network protocols and distributed computing technologies (e.g. DOM, DCOM, HTTP, FTP, TCP/IP, CORBA, RMI, JNI, JavaBeans). In Web-based applications the unique and dominant technology is HTML, (HyperText Markup Language), and more recently Dynamic HTML and XML (Extensible Markup Language), used to construct Web pages. Web pages may or may not include scripts, modules, multimedia, or text content, but almost always comprise a proportion of HTML or DHTML, which specifies how a (client) Web page should be designed in a browser.


4.   Software metrics and effort estimation

Reliable software effort estimation and assessment is critical for project selection, project planning and project control. Software metrics are quantifiable measures that are used to measure specific attributes of a software system, software development resources, and/or the software development process. Software metrics are designed to give you a view of your software from some perspective- such as performance, design or maintainability. Software is measured (i) to indicate the quality of product, (ii) to assess the productivity of the people who produce the product, (iii) to assess the benefit derived from new software engineering tools and methods, (iv) to form a baseline for estimation, and (v) help to justify requests for new tools or training. A number of metrics have been proposed to quantify parameters like size, complexity, and reliability of software products. In reality, software metrics include much more than primitive measures of program size. Software metrics include calculations based on measurements of any or all components of software development phase. Metrics help us understand the technical process that is used to develop a product. The process is measured to improve it and the product is measured to increase quality. The following areas of software development can benefit from a well-planned metrics program: (i) Project management, (ii) Product quality, (iii) Product performance, (iv) Development process, and (v) Cost and schedule estimation. There are three types of metrics employed for software development- (i) Product level metrics: These metrics are used to quantify characteristics of software product being developed. In general product metrics describe the characteristics of product such as size, complexity, design features, performance, and quality level, (ii) Process level metrics: These metrics are used to quantify characteristics of the environment or the process being employed to develop software. In general process metrics can be used to improve software development and maintenance. Examples include the effectiveness of defect removal during development, the pattern of testing defect arrival, and the response time of the fix process. Process metrics are further divided into following metrics: (a) Life cycle metrics, (b) Management metrics, and (c) Maturity metrics. The Life cycle metrics are further classified as Problem definition metrics, Requirement analysis and specification metrics, Design metrics, Implementation metrics, & maintenance metrics [25-32]. The Management metrics are further classified as Project Management metrics (Milestone Metrics, Risk metrics, Workflow metrics, Controlling metrics, and Management database metrics), Quality Management metrics (Customer satisfaction metrics, Review metrics, Productivity metrics, Efficiency metrics, Quality assurance metrics, and Configuration Management metrics (Change control metrics, Version control metrics) [33-43]. The Maturity metrics are further classified as Organization metrics, Resource, personnel and raining metrics, Technology management metrics, Documented standards metrics, Process metrics, Data management and analysis metrics [44-49], and (iii) Project level metrics: These metrics describe the project characteristics and execution. Examples include the number of software developers, the staffing pattern over the life cycle of the software, cost, schedule, and productivity. Some metrics belong to multiple categories. For example, the in-process quality metrics of a project are both process metrics and project metrics. Project metrics can divide further in following metrics:  (a) Structure metrics, (b) Quality metrics, (c) Size metrics, (d) Architecture metrics, and (e) Complexity metrics. The Structure metrics are further classified as Component characteristics, Structure characteristics, and Psychological rules metrics [50-51]. The Quality metrics are further classified as Functional metrics, Reliability metrics, Usability metrics, Efficiency, Maintainability metrics, and Portability metrics [52-54]. The Size metrics are further classified as Number of elements, Development metrics, and Size of components [51], [55]. The Architecture metrics are further classified as components metrics, Architecture characteristics and Architecture standard metrics [51], [56]. The Complexity metrics are further classified as Computational complexity metrics, and Psychological complexity metrics [57-58]. Besides these there are some software metrics (Performance metrics, paradigm metrics, and Replacement metrics) and Personnel metrics (Programming experience metrics, Communication level metrics, Productivity metrics, and Team structure metrics [58-62].


5.  Support for defining Web-based effort measurement

The frameworks will be based on a series of checklists that a Web-based project uses simultaneously for issue identification and measurement definition as follows [63]: (i)  The size checklist: This checklist can be used to help define counts of physical and logical source lines of code in a Web-based application. There are attributes to separate development status (e.g., estimated or planned) and also for separating the data by language. Other attributes include: statement type, origin, usage, delivery, functionality, and replications, (ii) The effort measures checklist: This checklist can be used to help define counts of physical and logical source lines of code. There are attributes for: type of labor, hour information, employment class, labor class, activity, and product-level functions, (iii) The problem count checklist: this checklist can be used to help define counts of defects and enhancements regarding software products. There are attributes for: problem status, problem type, uniqueness, criticality, urgency, and finding attribute: e.g., design, code, inspections, reviews, or testing, (iv) The schedule measures checklist: This checklists allow the project to determine what milestones and deliverables it. In the schedule checklists there are capabilities to separate the items to be tracked by builds and overall. For each item to be tracked there is the capability to define exit criteria on that item and to further decompose that item to track key events regarding that item, for example, when sign-off by the user, management, and/or quality assurance is obtained. As part of the schedule series of checklists, the ability to track schedule and progress by counting completed work units is also included, and (v) Implementation issues: This issue translates design specifications into source code. The primary goal of implementation is to write source code and internal documentation so that conformance of the code to its specifications can easily be verified, and so that debugging, testing, and modification are eased. The implementation team should be provided with a well-defined set of software requirements, an architectural design specification, and a detailed design description. In a well-known experiment, Weinberg [64] gave five programmers five different implementation goals for the same program: minimize the memory required, maximize output readability, maximize source text readability, minimize the number of source statements, and minimize development time. 


6.   General evaluation of size measures for Web-based designs

Web designers recognize the importance of realistic estimates of effort to the successful management of software projects, the Web being no exception. Estimates are necessary throughout the whole development life cycle. They are fundamental used to determine a project’s feasibility in terms of cost-benefit analysis and design, and to manage resources effectively. The Size measures can be described in terms of length, functionality and complexity is often a major determinant of estimations. Most estimates prediction models to date concentrate on functional measures of size, although length and complexity are also essential aspects of size in order to analyze the overall effect of Parametric influence (including quantitative parameters such as size, number of defects, months and qualitative parameters such as complexity, speed, required reliability, tool usage, and analyst capability), sensitivity, risk identification, software reuse and COTS (Commercial-Off-the-Shelf based Systems). So an over all case study evaluation is required to predicts the size metrics, characterizing length, complexity and functionality for Web design and estimations [65]. The parametric influence including both qualitative and quantitative study predicts through a case study evaluation and hypothetical analysis where a set of proposed or reused size metrics for estimation prediction will have to be measured [66-69]. To date there are only a few examples of estimation prediction models for Web development in the literature as most work proposes methods and tools as a basis for process improvement and higher product quality.


6.1   Size metrics for web-based design and estimation

The Web-based designs and estimation activities should be based upon conceptual framework for software measurement, which is based on following principles: (a) Determining relevant measurement goals, (b) Recognizing the entities to be examined, (c) Identifying the level of maturity the organization has reached, and (d) Classifying the functionality of metrics through standardization.


6.2   Factors involved in Web-based design

(a) Create Web pages that conform to accepted and published standards for measuring the HTML, CSS, XML and other Web based specifications. These are much more likely to be interpreted correctly by the various user agents (browsers) that exist. Additionally, if style sheets are used, you should conform to number of measurements including absolute units such as inches, centimeters, points, and so on, as well as relative measures such as percentage and em units [2]; (b) Know the difference between structural and presentation elements, use stylesheets when appropriate. It should be noted, though, that stylesheets support is not fully implemented on all user agents (browsers); this means that for at least the near future, some presentation elements in HTML will still be used and easy to measure. Moreover, if a Web-based design contains the multiple links and these links are further connected to different stylesheets then measurements will be more complex. So always try to use a single stylesheets for an effective Web-based design; (c) The Web-based design should include the rich Meta-Content about the purpose and function of elements by providing the valuable tools for giving additional information on the function and meaning of various tags in the larger scope of your page. It can increase the accessibility of Web page; (d) Make sure your pages can be navigated by keyboard. It means Web-based design measurements should also be based on the keyboard navigation; (e) Provide alternative methods to access non-textual content, including images, scripts, multimedia tables, forms and frames, for user agents that don’t display them. The foremost example of this is the “ALT” attribute, of the <IMG> tag, which allows an author to provide alternative text in case a user agent can’t display graphics. Accessibility on some Web design can also be measured and maintained by providing off-line or at least, off-Web methods of doing things; such as providing an e-mail link or response form, for whatever reason; (f) Be wary of common pitfalls that can reduce the accessibility, while measuring the Web design of your site. Examples of these pitfalls include: (i) Blinking text (ii) Use of ASCII art  (iii) Link names that don’t make sense out of content  (iv) Link that aren’t separated by printable characters  (v) Use of platform dependent scripting; and (g) For effective Web measurements, it is always better to define the functions in the <HEAD> tag to reduce the complexity and the efforts required. In this way, security will be improved and this type of Web-design will be more reliable.

In general, all the Web measurements performed on the three broad categories of Web documents, generally simplicity, reliability, and performance (SRP) is tested and measured. For dynamic Web documents, instead of checking SRP generally cost, complexity, and speed of retrieval of information is verified. For active Web documents generally its ability to update information continuously is checked and measured.


7.   Existing work for Web effort estimation

From the beginning of software engineering, several development effort estimation methods have been proposed. We can classify these methods for our research as those for traditional software and those for Web-oriented software. The traditional effort estimation methods are used to estimate the development effort of software that consists of programs in a programming language, which eventually interact with data files or databases. On the other hand, the Web-oriented methods use different metrics and they are focused on estimating the development effort of products that are event-oriented. These products generally involve code in a programming language, imagery, look-and-feel, information structure, navigation and multimedia objects. Several size metrics have been proposed for Web applications, like Object Points, Application Points and Multimedia Points [16]. However, the most appropriate seems to be Web Objects (WO) [1]. WOs are an indirect metric that is based on a predefined vocabulary that allows defining Web systems components in term of operands and operators. To estimate the amount of WOs that are part of a Web-based application it is necessary to identify all the operators and operands present in the system. Then, they are categorized using a predefined table of Web Objects predictors and also they are classified in three levels of complexity: low, average and high. The final amount of WO in a Web-based application is computed using the Halstead’s equation [12] (see equation 2), and it is known as the volume or size of the system.



Effort = A ĺ Ci (Size)P1    Duration = B (Effort)P2                                                   (1)



Where: A is effort coefficient, B = duration coefficient, Ci = cost drivers, P1 = effort power law, and P2 = duration power law.


V = N log2 (n) = (N1 + N2) log2 (n1 + n2)                                                 (2)


Where: N = number of occurrences of Operands/ Operators, n = number of distinct Operands/ Operators, N1 = total occurrences of Operand estimator, N2 = total occurrences of Operator estimator, n1 = number of unique Operands estimator, n2 = number of unique Operator estimator, and V = volume of work involved represented as Web objects. The effort estimation and the duration of the development are computed using WebMo (Web Model), which is an extension of COCOMO II [18]. This model uses two constants, two power laws, several cost drivers, and the product size expressed in WO (see equation 1).  Constants A and B, and power laws P1 and P2 are defined by a parameter table in the model. This model contains the values obtained from a database of former projects (historical information). The cost drivers are parameters used to adjust the effort and duration in terms of the development scenario. For this model nine cost drivers were defined: product reliability and complexity (RCPX), platform difficulty (PDIF), personnel capability (PERS), personnel experience (PREX), facilities of tools and equipment (FCIL), scheduling (SCED), reuse (RUSE), teamwork (TEAM) and process efficiency (PEFF) [18]. Each cost driver has different values that may be: very low, low, normal, high, and very high.  The combination of WebMo (WebMo equation 1) and Web Objects (Halstead’s equation 2) is, at this moment, the most appropriate method to estimate the development effort of Web applications. However, this combination does not seem to be the best for accurate and frequent development scenarios because it needs an important amount of historical detailed information to carry out the estimation. Also, the WO identification and categorization process is difficult to carry out in a short time, and it requires an expert that also knows how to carry out the project in critical technical decisions. The above effort estimation methods presented are not appropriate to estimate the development effort of Web-based information systems in different scenarios.


8.   The RSWAEA Method

In order to deal with the problem of effort estimation we analyzed Web-based software development processes, related to the development of small and medium size Web-based information systems. Based on the analysis of these results, we identified a low usability of the well-known effort estimation methods and a necessity of a model to support estimation in such scenario. Due to this, we developed a method for fast estimating the Web-based software development effort and duration, which will definitely be adapted by the software community for the development of Web-based hyper media applications. We called it RS Web Application Effort Assessment (RSWAEA) method. The method will be very useful to estimate the development effort of small to large-size Web-based information systems. The DWOs (Data Web Objects) are an approximation of the whole size of the project; so, it is necessary to know what portion of the whole system DWOs represent. This knowledge is achieved through a relatively simple process (briefly described in Part-II of this paper). Assuming that the estimation factors in the computation of the effort are subjective, flexible and adjustable for each project, the role of the expert becomes very relevant. Once the value of the portion or representativeness is calculated, the expert can adjust the total number of DWOs and he/she can calculate the development effort using the following equation.




E= (DWO . (1+X*))P . CU . P cdi                                                                          (3)


Where: E is the development effort measured in man-hours, CU is the cost of user, cdi is the cost drivers, DWO corresponds to the Web application size in terms of data web objects, X* is the coefficient of DWO representativeness, and P is a constant. The estimated value of real data web objects (DWO*) is calculated as the product of the initial DWOs and the representativeness coefficient X*. This coefficient is a historical value that indicates the portion of the final product functionality that cannot be inferred from the system data model. The process of defining such coefficient is presented in the next section. The cost of user is the values between 0 and 5. A value of CU of 0 means the system reuses all the functionality associated with each user type; so, the development effort will also be zero. On the other hand, if the cost of user is five, this means that there is no reuse of any kind to implement the system functionality for each user type. It represents the system functionality that is associated with each user type. The defined cost drivers (cdi) are defined in part- II of this paper) and they are similar to those defined by Reifer for WebMo [13]. The last adjustable coefficient in RSWAEA corresponds to constant P that is the exponent value of the DWO*. This exponent is a value very close to 1.01, and it must neither be higher than 1.12 nor lower than 0.99. This constant’s value depends on the project size measured in DWOs. In order to determine this value, various statistical analyses have been done on various Web-based applications. As a result, this constant was assigned the value 1.09 for projects smaller than 300 DWOs, and 1.03 for projects larger than 300 DWOs.


9.   Measurements and validations

On the basis of above mentioned Web-based Designs and projected measurements techniques, the following things can be examined and calculated for predicting the efficient Web-based design:  (a) Identification of measures that can be used to predict the efforts for Web-design; (b) Identification of a model that can be used to predict the efforts required using above-said measures; and (c) Identification of a methodology that can help the Webmasters in controlling the efforts for Web-design. These three things will be discussed and implemented in the part-II of this paper.

Our major aim is to bring light to this issue by identifying size metrics, features, functions, and cost drivers for early Web cost estimation based on current practices of several Web pages worldwide. This has been achieved using surveys (based upon hypothetical and Web companies based analysis). These proposed Web-based measurements techniques would be organized into their categories and rankings. On the basis of these, the above metrics and methods have been proposed to fast estimate the development effort of Web-based information systems. The proposed method use raw historical information about development capability and high granularity information about the system to be developed, in order to carry out such estimations. Generally, these estimations are the basis of the budget given to the client. Based on such budget the software development companies sign contracts with the client. Without an appropriate model, cost estimation is done with a high uncertainty and the development effort estimation relies only on the experience of an expert, whose estimations are generally not formally documented. At last, based on the basis of these results, we will identify a low usability of the well-known effort estimation methods and a necessity of a model to support estimation in such scenario. Due that, we will develop an embedded method by using the above-mentioned metrics and methods for fast estimating the Web-based software development effort and duration, which will be adapted to development of Web-based projects. These methods will specifically applicable to estimate the development effort of small, medium, or larger-size Web-based information systems in immature development scenarios. Furthermore, for Web-based software effort estimations and measurements, the compatibility, usability, maintainability, complexity, cost, configuration, time requirements, types of interfaces, tractability, and type of nature of Web design would also be examined and considered. Finally, we will validate this study on the basis of Web-based measurements by taking the features and functionality of the application to be developed for effort predictions in order to propose efficient Web-based measurements. The task size and consequences of estimation errors will be predicted. However, positive results would suggest that the various efforts applied to estimate Web-based applications, would be an invincible task for the upcoming future. Generally, the developers spend time trying to estimate the software development effort realistically and reliably, they usually have very little time for this task and very little historical information is available. These characteristics tend to make estimations less reliable regarding both time and cost. An expert knows the development scenario and the development capabilities of his/her organization, but he/she generally does not have good tools to support an accurate, reliable and fast estimation. In order to get fast and reliable effort estimations of Web-based applications, the part-II of this paper presented the RSWAEA method and UBMH to scientifically identify the effort assessment, estimation and failure points in Web systems.


10.   Acknowledgments

A major part of the research reported in this paper is carried out at U.I.E.T and D.C.S.A, K.U.K, Haryana, India. We are highly indebted and credited by gracious help from the Ernet section of K.U.K for their constant support. The authors would like to thank those nameless individuals who worked hard to supply the data.


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About the authors:

Sanjeev Dhawan is Lecturer in Computer Science & Engineering at the Kurukshetra University, Kurukshetra, Haryana. He has done his postgraduates degrees in Master of Science (M. Sc.) in Electronics Master of Technology (M. Tech.) in Computer Science & Engineering, and Master of Computer Applications (M.C.A) from the Kurukshetra University. At present he is pursuing PhD in Computer Science from Kurukshetra University. His current research interests include web engineering, advanced computer architectures, Intel microprocessors, programming languages and bio-molecular level computing.

Rakesh Kumar received his PhD in Computer Science and M.C.A from Kurukshetra University, Kurukshetra, Haryana. He is currently Senior Lecturer at the Department of Computer Science & Application, Kurukshetra University. His current research focuses on programming languages, information retrieval systems, software engineering, artificial intelligence, and compilers design.


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