Sajid Anwar

Automated GUI Test Coverage Analysis Using GA

A Graphical User Interface (GUI) is a graphical front-end to a software system. A GUI contains graphical objects with certain distinct values which can be used to determine the state of the GUI at any time. Software developing organizations always desire to test the software thoroughly to get maximum confidence about its quality. But this requires gigantic effort to test a GUI application due to the complexity involved in such applications. This problem has led to the automation of GUI testing and different techniques have been proposed for automated GUI Testing. Event-flow graph is a fresh technique being used in the field of automated GUI testing. Just as control-flow graph, another GUI model that represents all possible execution paths in a program, event-flow model, in the same way, represents all promising progressions of events that can be executed on the GUI. Another challenging question in software testing is, “How much testing is enough?” As development proceeds, there are fewer measures available that can be used to provide guidance on the quality of an automatic test suite. Genetic algorithm searches for the best possible test parameter combinations that are according to some predefined test criterion. Usually this test criterion corresponds to a “coverage function” that measures how much of the automatically generated optimization parameters satisfies the given test criterion. In this paper, we have attempted to exploit the event driven nature of GUI. Based on this nature, we have presented a GUI testing and coverage analysis technique centered on genetic algorithms.

Value Based Fuzzy Requirement Prioritization and Its Evaluation Framework

Requirement engineering is one of the most significant phases of software engineering. Success or failure of any software project relies heavily on better requirement engineering process. Better awareness of the requirements is fundamental for requirements engineering. Requirement Prioritization is an important component of requirement engineering process. In this paper, we have highlighted some serious shortcomings related to existing requirement prioritization techniques. Based on these findings, we have proposed an intelligent fuzzy logic based technique for requirements prioritization based on the perceived value of each requirement. We have also proposed a framework for evaluation of existing as well as proposed requirement prioritization techniques.

A Novel Fuzzy Logic Based Software Component Selection Modeling

Software component selection is the most important part of component based software development. A large amount of time is invested in searching and selecting the most appropriate component from component repository. Different methods are used to select components quickly and efficiently. In the proposed method we have used part of off the shelf option and fuzzy logic methodology for components selection. The proposed methodology incorporates several important factors such as efficiency, reusability, portability, functionality, security, testability and maintenance. The methodology is illustrated and evaluated using hypothetical case study.

A Prudent Based Approach for Customer Churn Prediction

This study contributes to formalize a three phase customer churn prediction technique. In the first phase, a supervised feature selection procedure is adopted to select the most relevant subset of features by laying-off the redundancy and increasing the relevance that leads to reduced and highly correlated features set. In the second phase, a knowledge based system (KBS) is built through Ripple Down Rule (RDR) learner which acquires knowledge about seen customer churn behavior and handles the problem of brittle in churn KBS through prudence analysis that will issue a prompt to the decision maker whenever a case is beyond the maintained knowledge in the knowledge database. In the final phase, a technique for Simulated Expert (SE) is proposed to evaluate the Knowledge Acquisition (KA) in KB system. Moreover, by applying the proposed approach on publicly available dataset, the results show that the proposed approach can be a worthy alternate for churn prediction in telecommunication industry.

Site selection for food distribution using rough set approach and TOPSIS method

Suitable site selection for a specific purpose is a crucial activity, and of the greatest importance to a project manager. Several methods have been proposed by the research community for effective site selection, but all proposed methods incur high costs. This study explores the combination of a rough set theory approach (RSTA) with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for suitable site selection for food distribution. This method provides a set of rules to determine different sites, which ultimately can help management develop strategies for suitable site selection. A set of rules for suitable site selection are derived from information related to a practical case, Pakistan Red Crescent Society (PRCS), to demonstrate the prediction ability of RSTA. The results clearly demonstrate that the RSTA model can be a valuable tool for site identification. Rough set theory also assists management in making appropriate decisions based on their objectives while avoiding unnecessary costs. However, while RSTA provides rules to determine the best sites for food distribution, it does not pinpoint the best sites for food distribution. To be more precise and accurate, this work is extended to another multi-criteria decision-making technique solution: the TOPSIS method. By using this method, this study provides the best top priority site for food distribution of PRCS.

Software Component Selection Based on Quality Criteria Using the Analytic Network Process

Component based software development (CBSD) endeavors to deliver cost-effective and quality software systems through the selection and integration of commercially available software components. CBSD emphasizes the design and development of software systems using preexisting components. Software component reusability is an indispensable part of component based software development life cycle (CBSDLC), which consumes a significant amount of organization’s resources, that is, time and effort. It is convenient in component based software system (CBSS) to select the most suitable and appropriate software components that provide all the required functionalities. Selecting the most appropriate components is crucial for the success of the entire system. However, decisions regarding software component reusability are often made in an ad hoc manner, which ultimately results in schedule delay and lowers the entire quality system. In this paper, we have discussed the analytic network process (ANP) method for software component selection. The methodology is explained and assessed using a real life case study.

Project scheduling conflict identification and resolution using genetic algorithms (GA)

Project management has gained a lot of application in software development activity in the past two decades. It is now considered to be one of the most critical component of software development lifecycle. Project management is traditionally defined as the discipline of planning, organizing, and managing activities and resources for successful execution and completion of project goals and objectives. In this respect, project management holds a key position in satisfactory completion of projects. That is the reason that we have a complete knowledge domain we know as software project management (SPM). The main purpose of SPM is to achieve all the project goals and objectives while working within the constraints posed by project environment and stakeholders. These constraints include (but not limited to) time, scope, resources, resource allocation and optimization etc. Successful project planning involved careful selection and synchronization of resources in order to achieve satisfactory completion of projects. These resources include human resource, rime, infrastructure etc. While planning software projects, it is natural to be confronted with various conflicts in resource allocation. It becomes a very time consuming activity to identify and sort out these conflicts when project size is large and time constraints are severe. A good project management activity is one which can effectively foresee these conflicts and resolve them in an optimal fashion. Computationally intelligent techniques are a good candidate to be used for the purpose of automation of this task. In this paper, a genetic algorithm based technique for conflict identification and resolution for project activities has been proposed. The effectiveness and utility of such a technique has also been discussed in this paper. The technique has been subjected to extensive experimentation and results have been presented.

Software Maintenance Prediction Using Weighted Scenarios: An Architecture Perspective

Software maintenance is considered one of the most important issues in software engineering which has some serious implications in term of cost and effort. It consumes enormous amount of organizations overall resources. On the other hand, software architecture of an application has considerable effect on quality factors such as maintainability, performance, reliability and flexibility etc. Using software architecture for quantification of certain quality factor will help organizations to plan resources accordingly. This paper is an attempt to predict software maintenance effort at architecture level. The method takes requirements, domain knowledge and general software engineering knowledge as input in order to prescribe application architecture. Once application architecture is prescribed, then weighted scenarios and certain factors (i.e. system novelty, turnover and maintenance staff ability, documentation quality, testing quality etc) that affect software maintenance are applied to application architecture to quantify maintenance effort. The technique is illustrated and evaluated using web content extraction application architecture.

A Comparison of Two Oversampling Techniques (SMOTE vs MTDF) for Handling Class Imbalance Problem: A Case Study of Customer Churn Prediction

Predicting the behavior of customer is at great importance for a project manager. Data driven industries such as telecommunication industries have advantage of various data mining techniques to extract meaningful information regarding customer’s future behavior. However, the prediction accuracy of these data mining techniques is significantly affected if the real world data is highly imbalanced. In this study, we investigate and compare the predictive performance of two well-known oversampling techniques Synthetic Minority Oversampling Technique (SMOT) and Megatrend Diffusion Function (MTDF) and four different rule generation algorithms (Exhaustive, Genetic, Covering, and LEM2) based on rough set classification using publicly available data sets. As useful feature extraction can play a vital role not only in improving the classification performance, but also to reduce the computational cost and complexity by eliminating unnecessary features from the dataset. Minimum Redundancy Maximum Relevance (mRMR) technique has been used in the proposed study for feature extraction which not only selects the best feature subset but also reduces the features space. The results clearly demonstrate the predictive performance of both oversampling techniques and rules generation algorithms that will help the decision makers/researcher to select the ultimate one.

Ontology driven semantic annotation based GUI testing

One major agreed upon factor responsible for popularity of software systems, is graphical user interface. Besides the efforts and desires of development organizations, testing a graphical user interface thoroughly, is still almost a nightmare. Manual effort required to complete this task is very large. One major breakthrough to automate this manual effort of GUI testing is to map GUI events with some models and graphs. Event-flow graph is relatively a fresh and useful addition to cope up with automation of GUI testing. In this paper we are presenting an idea of using ontology for GUI testing. This ontology is supposed to work on the basis of semantics of possible events and then annotations will be used to generate the test cases and work as an oracle for verification of the output of testing effort. This work still is based on theoretical concepts and needs practical verification, which will be completed in short time.