This paper presents a systematic data-driven approach to assisting situational application development. We first propose a technique to extract useful information from multiple sources to abstract service capabilities with set tags. This supports intuitive expression of user’s desired composition goals by simple queries, without having to know underlying technical details. A planning technique then exploits composition solutions which can constitute the desired goals, even with some potential new interesting composition opportunities. A browser-based tool facilitates visual and iterative refinement of composition solutions, to finally come up with the satisfying outputs. A series of experiments demonstrate the efficiency and effectiveness of our approach. Data-driven composition technique for situational web applications by using tag-based semantics in to illustrate the overall life-cycle of our “compose as-you-search” composition approach, to propose the clustering technique for deriving tag-based composition semantics, and to evaluate the composition planning effectiveness, respectively.
Compared with previous work, this paper
is significantly updated by introducing a semi-supervised technique for
clustering hierarchical tag based semantics from service documentations and
human-annotated annotations. The derived semantics link service capabilities
and developers’ processing goals, so that the composition is processed by
planning the “Tag HyperLinks” from initialquery to the goals. The planning
algorithm is also further evaluated in terms of recommendation quality,
performance, and scalability over data sets from real-world service repositories.
Results show that our approach reaches satisfying precision and high-quality
composition recommendations. We also demonstrate that our approach can accommodate
even larger size of services than real world repositories so as to promise
performance. Besides, more details of our interactive development prototyping
are presented. We particularly demonstrate how the composition UI can help
developers intuitively compose situational applications, and iteratively refine
their goals until requirements are finally satisfied.
1.2 INTRODUCTION:
We develop and deliver software systems more quickly, and these systems must provide increasingly ambitious functionality to adapt ever-changing requirements and environments. Particularly, in recent a few years, the emergence and wide adoption of Web 2.0 have enlarged the body of service computing research. Web 2.0 not only focuses on the resource sharing and utilization from user and social perspective, but also exhibits the notion of “Web as a Platform” paradigm. A very important trend is that, more and more service consumers (including programmers, business analysts or even endusers) are capable of participating and collaborating for their own requirements and interests by means of developing situational software applications (also noted as “situated software”).
Software engineering perspective, situational software applications usually follow the opportunistic development fashion, where small subsets of users create applications to fulfill a specific purpose. Currently, composing available web-delivered services (including SOAP based web services, REST (RE presentational State Transfer) web services and RSS/Atom feeds) into a single web applications, or so called “service mashups” (or “mashups” for short) has been popular. They are supposed to be flexible response for new needs or demands and quick roll-out of some potentially unanticipated functionality. To support situational application development, a number of tools from both academia and industry have emerged.
However, we argue that, the large number of available services and the complexity of composition constraints make manual composition difficult. For the situational applications developers, who might be non-professional programmers, the key challenge remained is that they intend to represent their desired goals simply and intuitively, and be quickly navigated to proper service that can response their requests. They usually do not care about (or understand) the underlying technical details (e.g., syntactics, semantics, message mediation, etc). They just want to figure out all intermediate steps needed to generate desired outputs.
Moreover, many end-users may have a general wish to know what they are trying to achieve, but not know the specifics of what they want or what is possible. It means that the process of designing and developing the situational application requires not only the abstraction of individual services, but also much broader perspective on the evolving collections of services that can potentially incorporate with current onesWe first present a data-driven composition technique for situational web applications by using tag-based semantics in ICWS 2011 work.
The main contributions in this paper are to illustrate the overall life-cycle of our “composeas-you-search” composition approach, to propose the clustering technique for deriving tag-based composition semantics, and to evaluate the composition planning effectiveness, respectively. Compared with previous work, this paper is significantly updated by introducing a semi-supervised technique for clustering hierarchical tag-based semantics from service documentations and human-annotated annotations. The derived semantics link service capabilities and developers’ processing goals, so that the composition is processed by planning the “Tag HyperLinks” from initialquery to the goals.
The planning algorithm is also further evaluated in
terms of recommendation quality, performance, and scalability over data sets
from real-world service repositories. Results show that our approach reaches satisfying
precision and high-quality composition recommendations. We also demonstrate
that our approach can accommodate even larger size of services than real world
repositories so as to promise performance. Besides, more details of our
interactive development prototyping are presented. We particularly demonstrate
how the composition UI can help developers intuitively compose situational
applications, and iteratively refine their goals until requirements are finally
satisfied.
1.3 SCOPE OF THE PROJECT
User-oriented abstraction: The tourist uses tags to represent their desired goals and find relevant services. Tags provide a uniform abstraction of user requirements and service capabilities, and lower the entry barrier to perform development.
Data-driven development: In the whole development process, the tourist selects or inputs some tags, while some relevant services are recommended. This reflects a “Compose-as-you-Search” development process. Recommended services either process these tags as inputs, or produce these tags as outputs. As shown in Fig. 1, each service has some inputs and outputs, which are associated with tagged data. In this way, services can be connected to build data flows. Developers can search their goals by means of tags, and compose recommended services in a data driven fashion.
Potential composition navigation: The developer is always assisted with possible composition suggestions, based on the tags in the current goals. Thecomposition engine interprets the user queries and automatically generates some appropriate compositions alternatives by a planning algorithm (Section 4). The recommendations not only contain the desired outputs from the developers’ goals, but also suggest some interesting or relevant suggestions leading to potential new composition possibilities.
For example, the tag “Italian” introduced the Google Translation service, which tourist was not aware of such composition possibility. In this way, the composition process is not like traditional semantic web services techniques which might need specific goals, but leads to some emergent opportunities according to current application situations.
1.4 LITRATURE SURVEY:
COMPOSING DATA-DRIVEN SERVICE MASHUPS WITH TAG-BASED SEMANTIC ANNOTATIONS
AUTHOR: X. Liu, Q. Zhao, G. Huang, H. Mei, and T. Teng
PUBLISH: Proc. IEEE Int’l Conf. Web Services (ICWS ’11), pp. 243-250, 2011.
EXPLANATION:
Spurred by Web 2.0 paradigm, there
emerge large numbers of service mashups by composing readily accessible data
and services. Mashups usually address solving situational problems and require
quick and iterative development lifecyle. In this paper, we propose an approach
to composing data driven mashups, based on tag-based semantics. The core
principle is deriving semantic annotations from popular tags, and associating
them with programmatic inputs and outputs data. Tag-based semantics promise a
quick and simple comprehension of data capabilities. Mashup developers
including end-users can intuitively search desired services with tags, and
combine several services by means of data flows. Our approach takes a planning
technique to retrieving the potentially relevant composition opportunities.
With our graphical composition user interfaces, developers can iteratively
modify, adjust and refine their mashups to be more satisfying.
TOWARDS AUTOMATIC TAGGING FOR WEB SERVICES
AUTHOR: L. Fang, L. Wang, M. Li, J. Zhao, Y. Zou, and L. Shao
PUBLISH: Proc. IEEE 19th Int’l Conf. Web Services, pp. 528-535, 2012.
EXPLANATION:
Tagging technique is widely used to
annotate objects in Web 2.0 applications. Tags can support web service
understanding, categorizing and discovering, which are important tasks in a
service-oriented software system. However, most of existing web services’ tags
are annotated manually. Manual tagging is time-consuming. In this paper, we
propose a novel approach to tag web services automatically. Our approach
consists of two tagging strategies, tag enriching and tag extraction. In the
first strategy, we cluster web services using WSDL documents, and then we
enrich tags for a service with the tags of other services in the same cluster.
Considering our approach may not generate enough tags by tag enriching, we also
extract tags from WSDL documents and related descriptions in the second step.
To validate the effectiveness of our approach, a series of experiments are
carried out based on web-scale web services. The experimental results show that
our tagging method is effective, ensuring the number and quality of generated
tags. We also show how to use tagging results to improve the performance of a
web service search engine, which can prove that our work in this paper is
useful and meaningful.
A TAG-BASED APPROACH FOR THE DESIGN AND COMPOSITION OF INFORMATION PROCESSING APPLICATIONS
AUTHOR: E. Bouillet, M. Feblowitz, Z. Liu, A. Ranganathan, and A. Riabov
PUBLISH: ACM SIGPLAN Notices, vol. 43, no. 10, pp. 585-602, Sept. 2008.
EXPLANATION:
In the realm of
component-based software systems, pursuers of the holy grail of automated
application composition face many significant challenges. In this paper we
argue that, while the general problem of automated composition in response to
high-level goal statements is indeed very difficult to solve, we can realize
composition in a restricted context, supporting varying degrees of manual to
automated assembly for specific types of applications. We propose a novel
paradigm for composition in flow-based information processing systems, where
application design and component development are facilitated by the pervasive
use of faceted, tag-based descriptions of processing goals, of component
capabilities, and of structural patterns of families of application. The facets
and tags represent different dimensions of both data and processing, where each
facet is modeled as a finite set of tags that are defined in a controlled
folksonomy. All data flowing through the system, as well as the functional
capabilities of components are described using tags. A customized AI planner is
used to automatically build an application, in the form of a flow of
components, given a high-level goal specification in the form of a set of tags.
End-users use an automatically populated faceted search and navigation
mechanism to construct these high-level goals. We also propose a novel software
engineering methodology to design and develop a set of reusable, well-described
components that can be assembled into a variety of applications. With examples
from a case study in the Financial Services domain, we demonstrate that
composition using a faceted, tag-based application design is not only possible,
but also extremely useful in helping end-users create situational applications
from a wide variety of available components.
CHAPTER 2
2.0 SYSTEM ANALYSIS
2.1 EXISTING SYSTEM:
In our previous work we have designed a technique to extract tags by mining service specifications (including WSDL, Web API documents and web pages that contain references to web services) and collecting human-generated contents (including comments and queries). Several web services tagging approaches have been proposed, for example the FCA tagging system most of them annotate web services manually. Manual tagging is a time consuming work. Moreover, several existing systems can recommend tags for web services based on existing handmade tags such as the approach these systems consider nothing about similarities between tags and web services. Another problem in these systems is that if there is no handmade tag, they cannot work at all. Another system CDKH in can generate tags for web services automatically, but the system doesn’t use existing handmade tags of web services.
These different ways are combined in
tagging tools that the tag-based platform facilitates. Moreover, inside of the
platform and due to the preferences of the users, different tagging behaviours
exist that actually obstruct the automated interoperability among tag sets.
Despite the fact that the systems offer solutions to aid the understanding of
the folksonomy that the users collectively build (tag clouds, tools based on
related tag ideas, collective intelligence methods, data mining, etc.) Although
tagging shows potential benefits, personal organization of information leads to
implicit logical conditions that often differ from the global one. Tagging
provides a sort of weak organisation of the information, very useful, but
mediated by the user’s behaviour. Therefore, it is also possible that user’s
tags associated with an object do not agree with the other users tags.
2.1.1 DISADVANTAGES:
2.2 PROPOSED SYSTEM:
We propose a heuristic graph-based planning algorithm within polynomial-time complexity. When the developer selects a tag from the tag cloud or input a keyword as the initial query request qi , the planning algorithm first computes the cost of achieving each tag starting from qi by conducting a forward search. Such a Depth-First Search step constructs all possible Tag Links that can perform the final goal. Based on the results above, the planning algorithm then approximates the sequence of Tag Links that connects qi to the final goal by a regression search step the tourist takes geographical locations of hotel, restaurant, bars and museum, we cannot give the reasonable order for visiting these places. Preferences, quality, ordering and other constraints would be helpful to improve the plan quality and performance. Due to the popularity and simplicity of tags, our tag-based service model can be extended, where all these constraints can be also presented as tags.
Our approach relies on the popularity of tags on the web. The primitive of tag-based composition of flow applications was first proposed in the MARIO system. Tag-based search is a hot topic in the research body of information retrieval and data mining. Most of existing research works focus on processing tags from popular social networking sites like Del.icio.us, Twitter and flickr. To best of our knowledge, few works have been made in the area of existing service-based applications. The primitive of tag-based composition of flow applications was first proposed in the MARIO system. Some recent works try to leverage tag-based service discovery, but not fully consider the hierarchy relationships of tags.
Our approach provides a systematic way for extracting useful tags from service documents and user generated annotations, by fully considering the unique features of web services like interface naming rules and developer preferences. Besides traditional similarity-based measurement, the clustering process is also controlled by the probability of tag occurrence and its own property, without any needs of training data. It should be noted that, we currently make simple mapping between our top-level tags to WordNet. However, the search results seem to be satisfying in regular cases.
2.2.1 ADVANTAGES:
2.3.1 HARDWARE REQUIREMENT:
CHAPTER 3
3.0 SYSTEM DESIGN:
Data Flow Diagram / Use Case Diagram / Flow Diagram:
External sources or destinations, which may be people or organizations or other entities
Here the data referenced by a process is stored and retrieved.
People, procedures or devices that produce data’s in the physical component is not identified.
Data moves in a specific direction from an origin to a destination. The data flow is a “packet” of data.
MODELING RULES:
There are several common modeling rules when creating DFDs:
3.1 ARCHITECTURE DIAGRAM
3.2 DATAFLOW DIAGRAM
ADMIN:
USER:
UML DIAGRAMS:
3.2 USE CASE DIAGRAM:
ADMIN:
USER:
3.3 CLASS DIAGRAM:
ADMIN:
USER:
3.4 SEQUENCE DIAGRAM:
ADMIN:
USER:
3.5 ACTIVITY DIAGRAM:
ADMIN:
USER:
CHAPTER 4
4.0 IMPLEMENTATION:
MARIO SYSTEM:
MARIO system is the most prior work to leverage tagbased descriptions as component annotations, whereby users can find desired goals by regular search. Based on the SPPL planner MARIO facilitates combination of components to create applications that satisfy end-user goals. Our approach shares common insights and learns successful experiences of MARIO. However, MARIO holds two assumptions: the tag-based semantics have to be prede- fined, and (2) the tag-based descriptions of all components might be (manually) pre-solved. The assumptions are quite reasonable to deal with relatively smaller component repository size or in a specific application domain with controlled vocabulary.
However, problems are yet remained in real-world scenarios: most of currently available web services and mashups are not with enough meaningful tags. In well-known repositories like ProgrammableWeb, 1 Seekda, 2 and Service-Finder, 3 existing tags are too limited and trivial to determine composition. For example, these tags can mainly help service categorization (like travel, education, games), but not provide sufficient enough information to reveal the relationships among services. In a sense, collecting enough tags and deriving semantics between them are indispensable step to achieve automated composition. Moreover, the quality of derived semantics should also be evaluated.
we have designed a technique to extract tags by mining service specifications (including WSDL, Web API documents and web pages that contain references to web services) and collecting human-generated contents (including comments and queries). Our work provides a similarity-based measurements including structure metric, lexical metric and frequency metric. We have obtained a repository with the size of 50,000 tags, which were extracted from over 20,000 real-world web services and 6,000 mashups. Initial experiments also showed that the tag-based search could improve the search performance and quality of a single web service. Based on the collected tags, this paper particularly addresses following three issues: (1) how to abstract tags for simple and precise service discovery; (2) how to identify the potential composition of a set of services by their tag-based descriptions; (3) how to operate the composition efficiently, even with the large size of tags.
4.1 ALGORITHM:
TAG CLUSTERING WITH ANNEALING ALGORITHM:
We apply a semi-supervised model to derive hierarchical structure from tags T annotating the services. It begins with the root node containing all tags in T and recursively splits them into a series of semantically meaningful clusters. The process does not terminate until each cluster represents a specific concept. At the final step of this algorithm, a cluster usually corresponds to a high-level category of a set of tags. For example, the tag set containing {country, street, city, milan, Italy, zipcode} represents the concept “geography”, and the one containing {rain, sunny, windchill, 27C, 80 F} is associated with the concept “weather”.
Our approach tries to generate a “Feature Tag” ô to summarize the semantics of other tags in the cluster (like “weather” in the example above), in order to navigate to high-level compositional semantics close to their desired goals. We briefly illustrate the splitting process as follows. At the beginning, we maintain a queue Q to store the information of all the nodes that are waiting for splitting, and a vector n in the queue indicates the probability that each tag emerges in this node. Initially all elements of n0 are assigned to 1, for all tags that are contained in the root node. In the clustering process, an annealing algorithm is employed to split the tags into several semantically meaningful clusters. Such optimizing algorithm could be stated as a process minimizing a predefined criterion.
4.2 MODULES:
DATA SET PREPARATION:
TAG-BASED SERVICE MODEL:
SEMANTICS DERIVATION:
TAG IDENTIFICATION AND EXTRACTION:
DATA-DRIVEN
COMPOSITION:
4.3 MODULE DESCRIPTION:
DATA SET PREPARATION:
Tag semantics play the crucial role in
our composition approach. We build up our service community in the Trustie
Project,4 which is a testbed environment for software service production. The
platform crawls web services and mashups from some well-known repositories like
ProgrammableWeb and Seekda. As our approach takes input/output as composition
unit, each API and each operation in WSDL is stored as one item. The data set
in this experiment includes 19,083 service items. These items were put into
some categories with statistics, like travel (728), news (484), weather
(1,491), maps (792), geography (1,822), food (273), photo (489), messaging
(816), blogging (332), and so on. Some sample services with their tags could be
found via our website. We first applied the splitting technique to extract tags
from textual descriptions. Then we manually filtered the redundant tags. For example,
the three tags “Map”, “Maps” and “Mapping” are considered as one tag. Finally
we chose a data set of 23,971 different tags. Applying algorithm for tag
clustering and the EM processing, we attained 594 clusters such as hotel,
geography, weather, search, map, etc.
TAG-BASED SERVICE MODEL:
Our data-driven, goal-oriented
composition technique in the key primitive is the tag-based data flow between
services. There are two constraints in terms of service composition: syntactic
and semantic. In our approach, semantic constraints can be inferred from the
hierarchical tag semantics; syntactic constraints depend on our underlying
composition middleware, which takes responsibility of dealing with actual data
types required and produced by the service. Based on the tag-based semantics,
if a web service ws1 can produce t1 as its output, and the service ws2 can
consume t1 or its father tag t2 as its input, we consider that ws1 and ws2 can
be composed, since a data flow can be created between them. From this perspective,
the tag-based service composition problem is defined as the result of creating
a data flow of a sequence of tags. Just like the hyperlinks form the navigation
among web pages, we call the tag-based data flow using the notion of Tag Link
(TL) in the following. The first precondition indicates the mapping and
propagation between web services at semantic level, which relies on the derived
tag-based hierarchy. The second precondition ensures that no extra data is left
at the syntactic level. Note that all selected services in the Tag-Links will
be encapsulated according to our iMashup component model and composition
runtime takes charge of interpreting and coordinating underlying technical
details such as data object types and structures.
SEMANTICS DERIVATION:
Generally, efficient service composition relies on the precision of candidate services that are discovered. The precision of candidate services search also reflects the quality of derived tag-based semantics. Hereby, we tried to evaluate the precision of our tag-based search. We compared the results with traditional Term Frequency-Inverse Document Frequency (TF/IDF) retrieval technique for searching a single web service. We computed the similarities between the input query and web services in formula 14 by referencing classic formula defined by Manning.
Our planning approach is the discovery
of potential composition opportunities. According to common experiences, the
number of candidate services often implies the number of concepts. As about 90
percent mashups on Programmable Web contain less than five services, to make a
comparison baseline, we chose 20 sample applications, each of which at least
contains eight services. For each sample application, we still employed the
same 20 junior students to manually extract the tags of the services, or add
new annotations based from tag-based taxonomy. For each output and the user
inputs, we ran the planning composition algorithm that might retrieve the same
output given the user inputs and form an application incrementally. We compared
the planned solutions with the original applications.
TAG IDENTIFICATION AND EXTRACTION:
Tags are actually a set of keywords to
describe some aspects of a service. We extract tags from two main sources: (1)
Service textual descriptions; (2) User-generated annotations. We briefly
illustrate how to process them for extracting tags. For textual descriptions
like WSDL documents, tags can be extracted from elements including SERVICES,
INTERFACE, MESSAGE, TYPE and DOCUMENTATION. Usually, useful tags reside in: (1)
service name containing the general information; (2) service interfaces
describing service usage (including operations and input/output messages). From
our investigation, we observe that over 90 percent WSDL documents use capital
letters, numbers, or “ ” to separate tokens in service names [16]. So we use
the following rules to split tokens into tags.
Capital letters, numbers, “%” and “ ” are treated as starting position
of a new word. First position is also
treated as a starting position of a new word.
Contiguous, single capital letters and numbers should be merged into one
token. For example, according to our rules, we split service name
“AmazonSimpleDB” into {Amazon, Simple, DB}. For service interfaces, they are
usually defined in form of “verb” plus “noun” e.g., “postZipcodeRequest”,
“getHotelInfoResponse”, “getCompanyInfoResponse”. Verbs can reflect the type of
messages: “post” and “request” are usually used for input messages, while “get”
and “return” are usually used for output messages. In contrast, nouns may
reflect more plentiful usage information of the services. So we extract the
nouns and ignore verbs. For example, consider the output message
“getHotelInfoResponse”, useful tags are {hotelname, address, zipcode,
telephone, tax}.
DATA-DRIVEN COMPOSITION:
Our development process can be generally described as following steps in Fig. 2: Tag extraction and clustering. Tags are extracted from multiple sources, including service textual documentation, user-generated comments and queries, etc. (step ❶). Browsing such a large size of tags is really tedious, and tag ambiguity might cause mistakes. Therefore, a semi-supervised technique is proposed to cluster tag-based taxonomy as uni- fied semantic foundation (step ❷). Composition semantics derivation. Service providers and application architects are responsible of annotating tagbased semantics to describe service capabilities, including functionalities, input and output data, and other useful information. Based on the generated tag hierarchy, some rules can help them accomplish the semantic annotation semi-automatically. This (step ❸) aims to make services compatibly composed by the Tag-Link model (Section 5.1). Composition goal search. In our browser-based development environment, developers can search their desired goals using tag queries (step ❹). As tags are easier and more intuitive to understand, developers only focus on their desired goals without having to know underlying technical information of services. The queries are immediately submitted to the composition engine. Composition planning. A composition engine interprets tag queries, and generates appropriate solutions that can contain or accomplish the goal (step ❺). Our composition engine employs a graph-based planning technique to generate possible composition recommendations. As discussed above, this process retrieves prefabricated composition logics from task templates, or generates potentially new alternatives. Recommendations might be either individual services, or a set of services connected by data flows. Composition visualization, refinement and refactoring. Developers are able to directly run the generated compositions within a browser-based environment. At each composition step, the developers can revise immediate composition results, and iteratively refractor or re-design composition results (steps ❻ and ❼). Such a feature makes developers know what exactly the current composition results are. In this way, they can visually and iteratively refine composition requirements, until the final outputs are satisfied. In other words, steps ❹ to ❼ are iteratively performed.
CHAPTER 5
5.0 SYSTEM STUDY:
5.1 FEASIBILITY STUDY:
The feasibility of the project is analyzed in this phase and business proposal is put forth with a very general plan for the project and some cost estimates. During system analysis the feasibility study of the proposed system is to be carried out. This is to ensure that the proposed system is not a burden to the company. For feasibility analysis, some understanding of the major requirements for the system is essential.
Three key considerations involved in the feasibility analysis are
5.1.1 ECONOMICAL FEASIBILITY:
This study is carried out to check the economic impact that the system will have on the organization. The amount of fund that the company can pour into the research and development of the system is limited. The expenditures must be justified. Thus the developed system as well within the budget and this was achieved because most of the technologies used are freely available. Only the customized products had to be purchased.
This study is carried out to check the technical feasibility, that is, the technical requirements of the system. Any system developed must not have a high demand on the available technical resources. This will lead to high demands on the available technical resources. This will lead to high demands being placed on the client. The developed system must have a modest requirement, as only minimal or null changes are required for implementing this system.
5.1.3 SOCIAL FEASIBILITY:
The aspect of study is to check the level of acceptance of the system by the user. This includes the process of training the user to use the system efficiently. The user must not feel threatened by the system, instead must accept it as a necessity. The level of acceptance by the users solely depends on the methods that are employed to educate the user about the system and to make him familiar with it. His level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system.
5.2 SYSTEM TESTING:
Testing is a process of checking whether the developed system is working according to the original objectives and requirements. It is a set of activities that can be planned in advance and conducted systematically. Testing is vital to the success of the system. System testing makes a logical assumption that if all the parts of the system are correct, the global will be successfully achieved. In adequate testing if not testing leads to errors that may not appear even many months.
This creates two problems, the time lag between the cause and the appearance of the problem and the effect of the system errors on the files and records within the system. A small system error can conceivably explode into a much larger Problem. Effective testing early in the purpose translates directly into long term cost savings from a reduced number of errors. Another reason for system testing is its utility, as a user-oriented vehicle before implementation. The best programs are worthless if it produces the correct outputs.
5.2.1 UNIT TESTING:
| Description | Expected result |
| Test for application window properties. | All the properties of the windows are to be properly aligned and displayed. |
| Test for mouse operations. | All the mouse operations like click, drag, etc. must perform the necessary operations without any exceptions. |
A program
represents the logical elements of a system. For a program to run
satisfactorily, it must compile and test data correctly and tie in properly
with other programs. Achieving an error free program is the responsibility of
the programmer. Program testing checks
for two types
of errors: syntax
and logical. Syntax error is a
program statement that violates one or more rules of the language in which it
is written. An improperly defined field dimension or omitted keywords are
common syntax errors. These errors are shown through error message generated by
the computer. For Logic errors the programmer must examine the output
carefully.
5.1.2 FUNCTIONAL TESTING:
Functional testing of an application is used to prove the application delivers correct results, using enough inputs to give an adequate level of confidence that will work correctly for all sets of inputs. The functional testing will need to prove that the application works for each client type and that personalization function work correctly.When a program is tested, the actual output is compared with the expected output. When there is a discrepancy the sequence of instructions must be traced to determine the problem. The process is facilitated by breaking the program into self-contained portions, each of which can be checked at certain key points. The idea is to compare program values against desk-calculated values to isolate the problems.
| Description | Expected result |
| Test for all modules. | All peers should communicate in the group. |
| Test for various peer in a distributed network framework as it display all users available in the group. | The result after execution should give the accurate result. |
5.1. 3 NON-FUNCTIONAL TESTING:
The Non Functional software testing encompasses a rich spectrum of testing strategies, describing the expected results for every test case. It uses symbolic analysis techniques. This testing used to check that an application will work in the operational environment. Non-functional testing includes:
5.1.4 LOAD TESTING:
An important tool for implementing system tests is a Load generator. A Load generator is essential for testing quality requirements such as performance and stress. A load can be a real load, that is, the system can be put under test to real usage by having actual telephone users connected to it. They will generate test input data for system test.
| Description | Expected result |
| It is necessary to ascertain that the application behaves correctly under loads when ‘Server busy’ response is received. | Should designate another active node as a Server. |
5.1.5 PERFORMANCE TESTING:
Performance tests are utilized in order to determine the widely defined performance of the software system such as execution time associated with various parts of the code, response time and device utilization. The intent of this testing is to identify weak points of the software system and quantify its shortcomings.
| Description | Expected result |
| This is required to assure that an application perforce adequately, having the capability to handle many peers, delivering its results in expected time and using an acceptable level of resource and it is an aspect of operational management. | Should handle large input values, and produce accurate result in a expected time. |
5.1.6 RELIABILITY TESTING:
The software reliability is the ability of a system or component to perform its required functions under stated conditions for a specified period of time and it is being ensured in this testing. Reliability can be expressed as the ability of the software to reveal defects under testing conditions, according to the specified requirements. It the portability that a software system will operate without failure under given conditions for a given time interval and it focuses on the behavior of the software element. It forms a part of the software quality control team.
| Description | Expected result |
| This is to check that the server is rugged and reliable and can handle the failure of any of the components involved in provide the application. | In case of failure of the server an alternate server should take over the job. |
5.1.7 SECURITY TESTING:
Security testing evaluates system characteristics that relate to the availability, integrity and confidentiality of the system data and services. Users/Clients should be encouraged to make sure their security needs are very clearly known at requirements time, so that the security issues can be addressed by the designers and testers.
| Description | Expected result |
| Checking that the user identification is authenticated. | In case failure it should not be connected in the framework. |
| Check whether group keys in a tree are shared by all peers. | The peers should know group key in the same group. |
5.1.8 WHITE BOX TESTING:
White box testing, sometimes called glass-box testing is a test case design method that uses the control structure of the procedural design to derive test cases. Using white box testing method, the software engineer can derive test cases. The White box testing focuses on the inner structure of the software structure to be tested.
| Description | Expected result |
| Exercise all logical decisions on their true and false sides. | All the logical decisions must be valid. |
| Execute all loops at their boundaries and within their operational bounds. | All the loops must be finite. |
| Exercise internal data structures to ensure their validity. | All the data structures must be valid. |
5.1.9 BLACK BOX TESTING:
Black box testing, also called behavioral testing, focuses on the functional requirements of the software. That is, black testing enables the software engineer to derive sets of input conditions that will fully exercise all functional requirements for a program. Black box testing is not alternative to white box techniques. Rather it is a complementary approach that is likely to uncover a different class of errors than white box methods. Black box testing attempts to find errors which focuses on inputs, outputs, and principle function of a software module. The starting point of the black box testing is either a specification or code. The contents of the box are hidden and the stimulated software should produce the desired results.
| Description | Expected result |
| To check for incorrect or missing functions. | All the functions must be valid. |
| To check for interface errors. | The entire interface must function normally. |
| To check for errors in a data structures or external data base access. | The database updation and retrieval must be done. |
| To check for initialization and termination errors. | All the functions and data structures must be initialized properly and terminated normally. |
All
the above system testing strategies are carried out in as the development,
documentation and institutionalization of the proposed goals and related
policies is essential.
CHAPTER 6
6.0 SOFTWARE DESCRIPTION:
Java technology is both a programming language and a platform.
With most programming languages, you either compile or interpret a program so that you can run it on your computer. The Java programming language is unusual in that a program is both compiled and interpreted. With the compiler, first you translate a program into an intermediate language called Java byte codes —the platform-independent codes interpreted by the interpreter on the Java platform. The interpreter parses and runs each Java byte code instruction on the computer. Compilation happens just once; interpretation occurs each time the program is executed. The following figure illustrates how this works.
You can think of Java byte codes as the machine code instructions for the Java Virtual Machine (Java VM). Every Java interpreter, whether it’s a development tool or a Web browser that can run applets, is an implementation of the Java VM. Java byte codes help make “write once, run anywhere” possible. You can compile your program into byte codes on any platform that has a Java compiler. The byte codes can then be run on any implementation of the Java VM. That means that as long as a computer has a Java VM, the same program written in the Java programming language can run on Windows 2000, a Solaris workstation, or on an iMac.
A platform is the hardware or software environment in which a program runs. We’ve already mentioned some of the most popular platforms like Windows 2000, Linux, Solaris, and MacOS. Most platforms can be described as a combination of the operating system and hardware. The Java platform differs from most other platforms in that it’s a software-only platform that runs on top of other hardware-based platforms.
The Java platform has two components:
You’ve already been introduced to the Java VM. It’s the base for the Java platform and is ported onto various hardware-based platforms.
The Java API is a large collection of ready-made software components that provide many useful capabilities, such as graphical user interface (GUI) widgets. The Java API is grouped into libraries of related classes and interfaces; these libraries are known as packages. The next section, What Can Java Technology Do? Highlights what functionality some of the packages in the Java API provide.
The following figure depicts a program that’s running on the Java platform. As the figure shows, the Java API and the virtual machine insulate the program from the hardware.
Native code is code that after you compile it, the compiled code runs on a specific hardware platform. As a platform-independent environment, the Java platform can be a bit slower than native code. However, smart compilers, well-tuned interpreters, and just-in-time byte code compilers can bring performance close to that of native code without threatening portability.
The most common types of programs written in the Java programming language are applets and applications. If you’ve surfed the Web, you’re probably already familiar with applets. An applet is a program that adheres to certain conventions that allow it to run within a Java-enabled browser.
However, the Java programming language is not just for writing cute, entertaining applets for the Web. The general-purpose, high-level Java programming language is also a powerful software platform. Using the generous API, you can write many types of programs.
An application is a standalone program that runs directly on the Java platform. A special kind of application known as a server serves and supports clients on a network. Examples of servers are Web servers, proxy servers, mail servers, and print servers. Another specialized program is a servlet.
A servlet can almost be thought of as an applet that runs on the server side. Java Servlets are a popular choice for building interactive web applications, replacing the use of CGI scripts. Servlets are similar to applets in that they are runtime extensions of applications. Instead of working in browsers, though, servlets run within Java Web servers, configuring or tailoring the server.
How does the API support all these kinds of programs? It does so with packages of software components that provides a wide range of functionality. Every full implementation of the Java platform gives you the following features:
The Java platform also has APIs for 2D and 3D graphics, accessibility, servers, collaboration, telephony, speech, animation, and more. The following figure depicts what is included in the Java 2 SDK.
We can’t promise you fame, fortune, or even a job if you learn the Java programming language. Still, it is likely to make your programs better and requires less effort than other languages. We believe that Java technology will help you do the following:
Microsoft Open Database Connectivity (ODBC) is a standard programming interface for application developers and database systems providers. Before ODBC became a de facto standard for Windows programs to interface with database systems, programmers had to use proprietary languages for each database they wanted to connect to. Now, ODBC has made the choice of the database system almost irrelevant from a coding perspective, which is as it should be. Application developers have much more important things to worry about than the syntax that is needed to port their program from one database to another when business needs suddenly change.
Through the ODBC Administrator in Control Panel, you can specify the particular database that is associated with a data source that an ODBC application program is written to use. Think of an ODBC data source as a door with a name on it. Each door will lead you to a particular database. For example, the data source named Sales Figures might be a SQL Server database, whereas the Accounts Payable data source could refer to an Access database. The physical database referred to by a data source can reside anywhere on the LAN.
The ODBC system files are not installed on your system by Windows 95. Rather, they are installed when you setup a separate database application, such as SQL Server Client or Visual Basic 4.0. When the ODBC icon is installed in Control Panel, it uses a file called ODBCINST.DLL. It is also possible to administer your ODBC data sources through a stand-alone program called ODBCADM.EXE. There is a 16-bit and a 32-bit version of this program and each maintains a separate list of ODBC data sources.
From a programming perspective, the beauty of ODBC is that the application can be written to use the same set of function calls to interface with any data source, regardless of the database vendor. The source code of the application doesn’t change whether it talks to Oracle or SQL Server. We only mention these two as an example. There are ODBC drivers available for several dozen popular database systems. Even Excel spreadsheets and plain text files can be turned into data sources. The operating system uses the Registry information written by ODBC Administrator to determine which low-level ODBC drivers are needed to talk to the data source (such as the interface to Oracle or SQL Server). The loading of the ODBC drivers is transparent to the ODBC application program. In a client/server environment, the ODBC API even handles many of the network issues for the application programmer.
The advantages
of this scheme are so numerous that you are probably thinking there must be
some catch. The only disadvantage of ODBC is that it isn’t as efficient as
talking directly to the native database interface. ODBC has had many detractors
make the charge that it is too slow. Microsoft has always claimed that the
critical factor in performance is the quality of the driver software that is
used. In our humble opinion, this is true. The availability of good ODBC
drivers has improved a great deal recently. And anyway, the criticism about
performance is somewhat analogous to those who said that compilers would never
match the speed of pure assembly language. Maybe not, but the compiler (or
ODBC) gives you the opportunity to write cleaner programs, which means you
finish sooner. Meanwhile, computers get faster every year.
6.6 JDBC:
In an effort to set an independent database standard API for Java; Sun Microsystems developed Java Database Connectivity, or JDBC. JDBC offers a generic SQL database access mechanism that provides a consistent interface to a variety of RDBMSs. This consistent interface is achieved through the use of “plug-in” database connectivity modules, or drivers. If a database vendor wishes to have JDBC support, he or she must provide the driver for each platform that the database and Java run on.
To gain a wider acceptance of JDBC, Sun based JDBC’s framework on ODBC. As you discovered earlier in this chapter, ODBC has widespread support on a variety of platforms. Basing JDBC on ODBC will allow vendors to bring JDBC drivers to market much faster than developing a completely new connectivity solution.
JDBC was announced in March of 1996. It was released for a 90 day public review that ended June 8, 1996. Because of user input, the final JDBC v1.0 specification was released soon after.
The remainder of this section will cover enough information about JDBC for you to know what it is about and how to use it effectively. This is by no means a complete overview of JDBC. That would fill an entire book.
Few software packages are designed without goals in mind. JDBC is one that, because of its many goals, drove the development of the API. These goals, in conjunction with early reviewer feedback, have finalized the JDBC class library into a solid framework for building database applications in Java.
The goals that were set for JDBC are important. They will give you some insight as to why certain classes and functionalities behave the way they do. The eight design goals for JDBC are as follows:
SQL Level API
The designers felt that their main goal was to define a SQL interface for Java. Although not the lowest database interface level possible, it is at a low enough level for higher-level tools and APIs to be created. Conversely, it is at a high enough level for application programmers to use it confidently. Attaining this goal allows for future tool vendors to “generate” JDBC code and to hide many of JDBC’s complexities from the end user.
SQL Conformance
SQL syntax varies as you move from database vendor to database vendor. In an effort to support a wide variety of vendors, JDBC will allow any query statement to be passed through it to the underlying database driver. This allows the connectivity module to handle non-standard functionality in a manner that is suitable for its users.
JDBC must be implemental on top of common database interfaces
The JDBC SQL API must “sit” on top of other common SQL level APIs. This goal allows JDBC to use existing ODBC level drivers by the use of a software interface. This interface would translate JDBC calls to ODBC and vice versa.
Because of Java’s acceptance in the user community thus far, the designers feel that they should not stray from the current design of the core Java system.
This goal probably appears in all software design goal listings. JDBC is no exception. Sun felt that the design of JDBC should be very simple, allowing for only one method of completing a task per mechanism. Allowing duplicate functionality only serves to confuse the users of the API.
Strong typing allows for more error checking to be done at compile time; also, less error appear at runtime.
Because more often than not, the usual SQL calls
used by the programmer are simple SELECT’s,
INSERT’s,
DELETE’s
and UPDATE’s,
these queries should be simple to perform with JDBC. However, more complex SQL
statements should also be possible.
Finally we decided to precede the implementation using Java Networking.
And for dynamically updating the cache table we go for MS Access database.
Java ha two things: a programming language and a platform.
Java is a high-level programming language that is all of the following
Simple Architecture-neutral
Object-oriented Portable
Distributed High-performance
Interpreted Multithreaded
Robust Dynamic Secure
Java is also unusual in that each Java program is both compiled and interpreted. With a compile you translate a Java program into an intermediate language called Java byte codes the platform-independent code instruction is passed and run on the computer.
Compilation happens just once; interpretation occurs each time the program is executed. The figure illustrates how this works.
The TCP/IP stack is shorter than the OSI one:
TCP is a connection-oriented protocol; UDP (User Datagram Protocol) is a connectionless protocol.
The IP layer provides a connectionless and unreliable delivery system. It considers each datagram independently of the others. Any association between datagram must be supplied by the higher layers. The IP layer supplies a checksum that includes its own header. The header includes the source and destination addresses. The IP layer handles routing through an Internet. It is also responsible for breaking up large datagram into smaller ones for transmission and reassembling them at the other end.
UDP is also connectionless and unreliable. What it adds to IP is a checksum for the contents of the datagram and port numbers. These are used to give a client/server model – see later.
TCP supplies logic to give a reliable connection-oriented protocol above IP. It provides a virtual circuit that two processes can use to communicate.
In order to use a service, you must be able to find it. The Internet uses an address scheme for machines so that they can be located. The address is a 32 bit integer which gives the IP address.
Class A uses 8 bits for the network address with 24 bits left over for other addressing. Class B uses 16 bit network addressing. Class C uses 24 bit network addressing and class D uses all 32.
Internally, the UNIX network is divided into sub networks. Building 11 is currently on one sub network and uses 10-bit addressing, allowing 1024 different hosts.
8 bits are finally used for host addresses within our subnet. This places a limit of 256 machines that can be on the subnet.
The 32 bit address is usually written as 4 integers separated by dots.
A service exists on a host, and is identified by its port. This is a 16 bit number. To send a message to a server, you send it to the port for that service of the host that it is running on. This is not location transparency! Certain of these ports are “well known”.
A socket is a data structure maintained by the system
to handle network connections. A socket is created using the call socket. It returns an integer that is like a file descriptor.
In fact, under Windows, this handle can be used with Read File and Write File
functions.
#include <sys/types.h>
#include <sys/socket.h>
int socket(int family, int type, int protocol);
Here “family” will be AF_INET for IP communications, protocol will be zero, and type will depend on whether TCP or UDP is used. Two
processes wishing to communicate over a network create a socket each. These are
similar to two ends of a pipe – but the actual pipe does not yet exist.
6.8 JFREE CHART:
JFreeChart is a free 100% Java chart library that makes it easy for developers to display professional quality charts in their applications. JFreeChart’s extensive feature set includes:
A consistent and well-documented API, supporting a wide range of chart types;
A flexible design that is easy to extend, and targets both server-side and client-side applications;
Support for many output types, including Swing components, image files (including PNG and JPEG), and vector graphics file formats (including PDF, EPS and SVG);
JFreeChart is “open source” or, more specifically, free software. It is distributed under the terms of the GNU Lesser General Public Licence (LGPL), which permits use in proprietary applications.
Charts showing values that relate to geographical areas. Some examples include: (a) population density in each state of the United States, (b) income per capita for each country in Europe, (c) life expectancy in each country of the world. The tasks in this project include: Sourcing freely redistributable vector outlines for the countries of the world, states/provinces in particular countries (USA in particular, but also other areas);
Creating an appropriate dataset interface (plus
default implementation), a rendered, and integrating this with the existing
XYPlot class in JFreeChart; Testing, documenting, testing some more,
documenting some more.
Implement a new (to JFreeChart) feature for interactive time series charts — to display a separate control that shows a small version of ALL the time series data, with a sliding “view” rectangle that allows you to select the subset of the time series data to display in the main chart.
There is currently a lot of interest in dashboard displays. Create a flexible dashboard mechanism that supports a subset of JFreeChart chart types (dials, pies, thermometers, bars, and lines/time series) that can be delivered easily via both Java Web Start and an applet.
The property editor mechanism in JFreeChart only
handles a small subset of the properties that can be set for charts. Extend (or
reimplement) this mechanism to provide greater end-user control over the
appearance of the charts.
CHAPTER 7
7.0 APPENDIX
7.1 SAMPLE SCREEN SHOTS:
7.2
SAMPLE SOURCE CODE:
CHAPTER 8
8.1 CONCLUSION
This paper presents our experiences of flowbased mashup development and tooling. The key principle of our approach is “Compose-as-you-Search”, which leverages tag-based service composition to lower the entry barrier for mashup development, and realizes the philosophy of “live development” by providing on-the-fly recommendations as well as the visual iterative refinement of applications. The key limitations of current approach are also addressed. As most service-oriented situational software applications are not appropriate for classic enterprise software that has strict quality requirements for security, availability, or performance. The approach proposed in this paper mainly targets at personal and small-scale data processing problems.
8.2 FUTURE ENHANCEMENT: One of our future directions is to accumulate useful composition knowledge. Composition knowledge retrieval is a recently hot topic in mashup development. We will attempt to combine our work with Knowledge Discovery from Service (KDS) proposed to extend the tag-based model to be more expressive beyond functionality specifications. For example, we can annotate a constraint by “tag “to the driving guide, to plan visiting orders. Certainly, the planning algorithm should add these constraints for scheduling actions.