Ali Ahsan

Exploration and analysis of regression test suite optimization

Regression Test Suite Optimization (RTO) is an active research area. A Regression Test Suite is always growing due to changes in software, which increases testing time. To save time and resources optimization of regression test suites is mandatory. Researchers have optimized test suites using conventional and Computational Intelligence based approaches and achieve optimization of regression test suites through selection techniques, minimization or reduction techniques and ranking or prioritization techniques. This paper surveys existing techniques for regression test suite optimization, various tools and mathematical models being used for RTO. During this survey we found many interesting facts about regression test suite optimization that will be shared in the conclusion.

Multi-objective regression test suite optimization with Fuzzy logic

Regression Testing is performed on already tested programs to ensure that modifications have not revealed defects into the unmodified portions of programs. Regression Test Suites are always growing due to addition of Test Cases. Many broken and redundant test cases are also part of regression test suites. Running the all regression test suite is always not feasible; therefore optimization of regression test suites is required to meet the constraints. In this work we proposed multi-objective optimization of regression test suites with Fuzzy Logic (Sugeno Model) for Black Box based testing methods. Proposed approach was implemented on two published case studies. Results indicates that optimization can be achieved by using Fuzzy Logic that is a safe technique for optimization and we can reduce the execution time and size of regression test suites up to 50%.

Project resource allocation optimization using search based software engineering — A framework

Human Resource Management is an important area of project management. The concept of Human Resource Allocation is not new and it can also be used for resource allocation to software projects. Software projects are more critical as compared to projects of other disciplines because success of software projects dependents on human resources. In software projects, Project Manager (PM) allocates resources and level resources using Resource Leveling techniques which are already implemented in various project management software. But, Resource Leveling is a resource smoothing technique not an optimization technique neither it ensures optimized resource allocation. Furthermore, Project duration and cost may increase after resource leveling. Therefore, resource leveling is not always a reliable method for resource allocation optimization. Exact solution of resource optimization problem cannot be determined because resource optimization is a NP-Hard problem. To solve resource optimization problems Search Based Software Engineering (SBSE) is used in various studies. However, in existing SBSE algorithms implementation for resource allocation optimization many objectives are not considered. Resource allocation optimization is a multi-objective optimization problem and many important factors like activity criticality, resource skills, activity precedence, skill required to perform activities must be addressed. Our research fills this gap and uses multiple objectives for resource allocation optimization which are 1: increase resource utilization, 2: decrease project duration and 3: decrease project cost. A framework and mathematical model for the implementation of resource allocation optimization is also proposed.

Neuro-Fuzzy Modeling for Multi-Objective Test Suite Optimization

Abstract Regression testing is a type of testing activity, which ensures that source code changes do not affect the unmodified portions of the software adversely. This testing activity may be very expensive in, some cases, due to the required time to execute the test suite. In order to execute the regression tests in a cost-effective manner, the optimization of regression test suite is crucial. This optimization can be achieved by applying test suite reduction (TSR), regression test selection (RTS), or test case prioritization (TCP) techniques. In this paper, we designed and implemented an expert system for TSR problem by using neuro-fuzzy modeling-based approaches known as “adaptive neuro-fuzzy inference system with grid partitioning” (ANFIS-GP) and “adaptive neuro-fuzzy inference system with subtractive clustering” (ANFIS-SC). Two case studies were performed to validate the model and fuzzy logic, multi-objective genetic algorithms (MOGAs), non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms were used for benchmarking. The performance of the models were evaluated in terms of reduction of test suite size, reduction in fault detection rate, reduction in test suite execution time, and reduction in requirement coverage. The experimental results showed that our ANFIS-based optimization system is very effective to optimize the regression test suite and provides better performance than the other approaches evaluated in this study. Size and execution time of the test suite is reduced up to 50%, whereas loss in fault detection rate is between 0% and 25%.

Recommended configuration management practices for freelance software developers

Configuration Management (CM) is an important activity throughout the software development life cycle (SDLC). CM becomes essential if any of the artifacts is to be changed during the life cycle. There are many practices which can be adopted in configuration management to stream line the work and bring the quality to the software development process. For this reason most of the software development companies also adopt configuration management practices with larger teams to implement these practices. The problem appears with the freelance software developers, where the numbers of people working in teams are a few. This is an investigative study which is carried out to find if the freelance software developers adopt the configuration management practices. Moreover, the purpose of this study is to find out the configuration management practices freelance software developers suggest are important and should be done by the freelance software developers. The study was done by distributing an open ended questionnaire to the freelance software developers working in a team of 5-30. The results show that although the freelance software developers do not implement all the configuration management practices, but they do suggest a few practices which they feel are important for them to carry out their work efficiently.

Expertise based skill management model for effective project resource allocation under stress in software industry of Pakistan

Concept of “Skill Management” is not new in context of human resource management. This concept can also be utilized for project management but unfortunately there is no well defined ** idea or model that software organizations can follow, and make effective use of the human skills for effective project resource allocation. Software professionals possess many skills and most of their skills become worthless because of their single job role†. Expertise based skill management system (EBSMS) is therefore required for proper management of skills for project resource allocation. This research addresses issues related to hard skills of people and corresponding best practices for management of these skills for effective project resource utilization and allocation in Software Industry of Pakistan and proposes EBSM Model.

Global Monitoring and Control: A Process Improvement Framework for Globally Distributed Software Development Teams

ABSTRACT Global software development (GSD), a fundamentally different paradigm from traditional software engineering, has observed continuous growth and lot of acceptance in the last few years. Organizations gain many advantages of GSD, but face additional challenges not observed in collocated teams’ environment. Literature reveals that existing process improvement frameworks do not explicitly accommodate the complex and challenging needs of GSD. In literature, it is found that one of the major challenges is weak monitoring and controlling of distributed teams. In this study, therefore, we developed a process improvement framework to improve the monitoring and controlling of distributed teams in a GSD environment. The proposed framework is constructed by using grounded theory methodology, and it is validated by using the methods of face validity, content validity, construct validity, convergent validity, and discriminant validity. The detailed analysis depicts that the proposed framework is a valid framework for global monitoring and control in a GSD environment.

Comparison of Search-Based Software Engineering Algorithms for Resource Allocation Optimization

Abstract A project manager balances the resource allocation using resource leveling algorithms after assigning resources to project activities. However, resource leveling does not ensure optimized allocation of resources. Furthermore, the duration and cost of a project may increase after leveling resources. The objectives of resource allocation optimization used in our research are to (i) increase resource utilization, (ii) decrease project cost, and (iii) decrease project duration. We implemented three search-based software engineering algorithms, i.e. multiobjective genetic algorithm, multiobjective particle swarm algorithm (MOPSO), and elicit nondominated sorting evolutionary strategy. Twelve experiments to optimize the resource allocation are performed on a published case study. The experimental results are analyzed and compared in the form of Pareto fronts, average Pareto fronts, percent increase in resource utilization, percent decrease in project cost, and percent decrease in project duration. The experimental results show that MOPSO is the best technique for resource optimization because after optimization with MOPSO, resource utilization is increased and the project cost and duration are reduced.

Value based Incremental Software Development

In todays competitive world, the investor of software product must rely on shorter development periods where product is developed in small features that result in early market delivery and revenue generation. The selection of specific feature for the current development period has remained a great challenge in the history of software development. The Incremental Funding Method (IFM) [1] is very famous technique that tries to choose the optimum delivery sequence of features, but it uses only one criterion that is Net Present Value. Our proposed methodology tries to evaluate multiple criteria for the selection of particular feature(s) using multi-criteria analysis method called desirability functions. Moreover, IFM methodology is silent about the uncertainty issue in future cash flows while our suggested methodology tries to fill this gap by means of an experts opinion (A fuzzy based technique). Another very important fact is that almost all cash flows have some interesting patterns/trends. It would be better to preserve these patterns that assist experts to better estimate the future cost estimates rather than starting blindly. A hypothetical case study is formulated in order to validate the extensions made in the IFM methodology.

The future of software engineering: a survey

Software Engineering (SE) is a new field compared to other sciences. The term Software Engineering first appeared in late 1950s. SE from its beginning has been continuously in the process of evolution. New approaches, methods, tools and techniques are introduced frequently. The future of Software Engineering is a hot topic and every year many publications discuss the same. The focus of this paper is to explore subareas of SE, predict the possible future of SE and provide a guide to practitioners to choose their careers according to the evolution of SE.