Mining software repositories for model-driven development

One major reason software development projects fail is that the development process is invisible. Managers tend to rely on meetings and reports to understand project status and make decisions, leading to mismanagement due to inaccurate or incomplete information. Software projects typically collect information during development using different tools and store it in repositories. We present a statistical process control (SPC) method of defining, collecting, and analyzing software metrics from software repositories for MDD process control and improvement (PCI). This method, which we call mining software repositories (MSR), can help us change the traditional, static, record-keeping use of software data repositories to a new, active use for predicting and planning various aspects of MDD projects.     

Adapting funding processes for agile IT projects: an empirical investigation

Agile software development that provides software development organizations, the ability to respond to changes in turbulent business environments, has been gaining wide adoption. Agile software development projects are characterized by ‘just enough’ planning and lack of upfront commitment to scope, cost, and schedule. These characteristics pose conflicting demands on managers responsible for making funding decisions, because traditional approaches to funding IT projects are often based on well-defined scope, cost, and schedule. These conflicts demand the adaptation of traditional funding processes to suit to agile projects. We draw from Adaptive Structuration Theory to understand the nature of conflicts between traditional IT project funding processes and the dynamic nature of agile projects, and how these conflicts are addressed by practices that are appropriated in the process of social interaction between funding decision makers and development teams. On the basis of a multisite case study, we present a framework that explains how organizations adapt traditional IT funding approaches to accommodate the unique characteristics of agile IT projects.     

A bayesian belief network based model for predicting software faults in early phase of software development process

It is always better to have an idea about the future situation of a present work. Prediction of software faults in the early phase of software development life cycle can facilitate to the software personnel to achieve their desired software product. Early prediction is of great importance for optimizing the development cost of a software project. The present study proposes a methodology based on Bayesian belief network, developed to predict total number of faults and to reach a target value of total number of faults during early development phase of software lifecycle. The model has been carried out using the information from similar or earlier version software projects, domain expert’s opinion and the software metrics. Interval type-2 fuzzy logic has been applied for obtaining the conditional probability values in the node probability tables of the belief network. The output pattern corresponding to the total number of faults has been identified by artificial neural network using the input pattern from similar or earlier project data. The proposed Bayesian framework facilitates software personnel to gain the required information about software metrics at early phase for achieving targeted number of software faults. The proposed model has been applied on twenty six software project data. Results have been validated by different statistical comparison criterion. The performance of the proposed approach has been compared with some existing early fault prediction models.     

Computer-aided software development process design

The authors describe an intelligent tool designed to aid managers of software development projects in planning, managing, and controlling the development process of medium- to large-scale software projects. Its purpose is to reduce uncertainties in the budget, personnel, and schedule planning of software development projects. It is based on dynamic models for the software development and maintenance life-cycle process. This dynamic process is composed of a number of time-varying, interacting developmental phases, each characterized by its intended functions and requirements. System dynamics is used as a modeling methodology. The resulting software life-cycle simulator (SLICS) and the hybrid expert simulation system of which it is a subsystem are described     

基于相似度的软件工程中软件成本估算问题研究

软件工程概念从1968年被提出以来,经历了近50年的发展,软件系统规模和复杂程度日益加大,然而从上个世纪70年代左右开始,软件工程领域出现大量软件项目进度延期、预算超支和质量缺陷为典型特征的软件危机.这体现出软件成本估算在软件工程开发过程的重要性.精准的软件成本估算是软件工程按时完成的保证.本文采用一种基于皮尔逊相关系数的相似度量方法,结合TOPSIS方法软件成本进行类比估算以获取与之最接近项目的项目作为参考进行软件成本估算.最后将该方法应用于Desharnais数据集进行实验,并和其他方法进行比较,实验结果表明,本文采用的基于相关系数的软件成本度量方法较已有的相似性度量方法准确度更好.     

Recommender system for software project planning one application of revised CBR algorithm

Project management is an experience-driven and knowledge-centralized activity. Therefore, project managers require some assistance to reduce the uncertainty at the early stage of constructing project plans. To overcome the predicament faced by project managers, this investigation proposes a hierarchical criteria architecture (HCA) to enable project managers to describe project requirements adequately. Furthermore, to solve HCA problems, a revised case-based reasoning (RCBR) algorithm, is presented and a recommender system for software project planning is implemented, based on multiple objectives decision techniques and the mining approach. Finally, the proposed RCBR algorithm is successfully applied to analyze 41 real projects from a software consultancy in Taiwan. Experimental results demonstrate that RCBR can efficiently provide related information to help project managers to construct project plans at an early stage. Additionally, the knowledge discovery process of RCBR provides project managers with results similar to what-if analysis. The knowledge can enable project managers to obtain feasible information to re-schedule project resources, and bargain with their customers in the early project planning stage.     

The optimization of success probability for software projects using genetic algorithms

The software development process is usually affected by many risk factors that may cause the loss of control and failure, thus which need to be identified and mitigated by project managers. Software development companies are currently improving their process by adopting internationally accepted practices, with the aim of avoiding risks and demonstrating the quality of their work.This paper aims to develop a method to identify which risk factors are more influential in determining project outcome. This method must also propose a cost effective investment of project resources to improve the probability of project success.To achieve these aims, we use the probability of success relative to cost to calculate the efficiency of the probable project outcome. The definition of efficiency used in this paper was proposed by researchers in the field of education. We then use this efficiency as the fitness function in an optimization technique based on genetic algorithms. This method maximizes the success probability output of a prediction model relative to cost.The optimization method was tested with several software risk prediction models that have been developed based on the literature and using data from a survey which collected information from in-house and outsourced software development projects in the Chilean software industry. These models predict the probability of success of a project based on the activities undertaken by the project manager and development team. The results show that the proposed method is very useful to identify those activities needing greater allocation of resources, and which of these will have a higher impact on the projects success probability.Therefore using the measure of efficiency has allowed a modular approach to identify those activities in software development on which to focus the project's limited resources to improve its probability of success. The genetic algorithm and the measure of efficiency presented in this paper permit model independence, in both prediction of success and cost evaluation.     

Software development cost estimation: Integrating neural network with cluster analysis

For software project planning control and management, an accurate estimate of software development cost is important. Past research has focused on using parametric models to predict development cost based on attributes such as lines of code or function points. This requires researchers to identify the set of factors that influence cost estimation before the system is constructed. We propose a non-parametric approach that integrates a neural network method with cluster analysis to estimate development cost. The integration of the two techniques not only allows for a more accurate cost estimate but also leads to an increase in the training efficacy of the network.     

Review of Intelligent Software Architectures for the Development of An Intelligent Decision Support System for Design Process Planning in Concurrent Engineering

Concurrent engineering (CE) is a strategy that attempts to process as many product development tasks in parallel and incorporate relevant life-cycle attributes as early as possible in the design phase in an effort to reduce the duration of design projects, save development costs, and provide better quality products. The CE environment is characterized by a high degree of distributed cognitive processing in the form of product development team structures. The distribution of appropriate knowledge to members of these teams and other participants in the design process for the purpose of supporting management and planning decisions is a considerably complex problem. New approaches and tools based on artificial intelligence methodologies are needed to deal with this level of complexity in coordinating knowledge resources. This paper reviews a number of potential candidates for an intelligent software architecture that can represent this type of problem as well as support the knowledge handling necessary to solve such problems. The selection of an appropriate architecture will support the development of an intelligent information system that is able to mimic human cognitive processes as the basic tool for providing decision-making support for planning and controlling a CE design process.     

A relationship-based and object-oriented software for monitoring management during geotechnical excavation

The efficient management of monitoring data is necessary for large geotechnical engineering projects. The development of an information management, prediction and warning software system for geotechnical monitoring is presented in this study. Seven categories of property objects that describe the hierarchical relationships among the monitoring objects, as well as two objects that represent and manage the construction progress, are proposed based on the requirements of geotechnical monitoring, data flow and the monitoring objectives of the site. The corresponding data structure and database were established using the object-oriented method in the Visual C++ environment. The software integrated various types of information and document management schemes, including data input and processing, CAD drawing visualisation, data modelling and prediction, as well as an early warning function. The applied case studies indicate that the software system is highly flexible and reliable and can be widely applied to monitor the sites of various geotechnical construction projects, such as tunnels, underground caverns, slopes and foundation pits.     

In-house software development: what project management practices lead to success?

Project management is an important part of software development, both for organizations that rely on third-party software development and for those whose software is developed primarily in-house. Moreover, quantitative survey-based research regarding software development's early, nontechnical aspects is lacking. To help provide a project management perspective for managers responsible for in-house software development, we conducted a survey in an attempt to determine the factors that lead to successful projects. We chose a survey because of its simplicity and because we hoped to find relationships among variables. Also, a survey let us cover more projects at a lower cost than would an equivalent number of interviews or a series of case studies. Our results provide general guidance for business and project managers to help ensure that their projects succeed.     

软件项目外包采购管理的探讨

分析了目前软件外包采购管理的重要意义和目前的形势，提出基于“双赢”策略的软件外包采购思想，在项目管理理论、CMM和ISO9000的基础上，提出和细化了软件项目外包采购管理的总体框架和具体操作内容，旨在通过对软件外包项目采购的选择购买、跟踪与控制、评估验收和项目后处理等过程的研究，来提高软件外包采购的项目管理水平，满足承包方对分承制方产品在质量，进度和成本等方面的要求和对外包过程的有效控制，为软件项目外包采购管理人员提供具体的操作过程。     

Determination of an implementation order for IS projects

The process of information systems (IS) planning in an organization occurs when there is a set of IS projects that require implementation, generally within certain time frames and budgetary constraints. We propose a method for determining an optimal order of project implementation. The proposed method utilizes the cost-benefit graph, which considers the expected cost and benefit of each of the projects comprising the set, as well as the weight (importance) of the cost and benefit factors to the decision-makers. The method allows us to analyze the sensitivity of a preferred implementation order to changes in cost and benefits, and the importance of these factors.     

面向快递终端的物流配送服务系统设计

针对传统的物流配送服务系统存在物流路径开销大、配送效率较低等问题,提出一种面向快递终端的物流配送服务系统改进设计方法。结合路径优化控制进行物流路径规划,建立物流配送服务网络移动快递终端节点分布模型,采用智能路径规划方法,实现物流配送服务系统的快递路径规划优化。在算法设计基础上,结合C/S面向对象模型进行物流配送服务系统的软件开发设计,物流配送服务系统的软件包括数据库管理模块、信息集成处理模块、人机交互控制模块和终端服务模块等,结合RFID射频识别和APP技术,实现系统优化设计。测试结果表明,采用该方法进行物流配送服务系统设计,降低了物流路径开销,提高了物流配送的效率。     

Operational release planning in large-scale Scrum with multiple stakeholders – A longitudinal case study at F-Secure Corporation

ContextThe analysis and selection of requirements are important parts of any release planning process. Previous studies on release planning have focused on plan-driven optimization models. Unfortunately, solving the release planning problem mechanistically is difficult in an agile development context.ObjectiveWe describe how a release planning method was employed in two case projects in F-Secure, a large Finnish software company. We identify the benefits which the projects gained from the method, and analyze challenges in the cases and improvements made to the method during the case projects.MethodWe observed five release planning events and four retrospectives and we conducted surveys in the first two events. We conducted six post-project interviews. We conjoined the observation notes, survey results and interviews and analyzed them qualitatively and quantitatively.ResultsThe focal point of the method was release planning events where the whole project organization gathered to plan the next release. The planning was conducted by the development teams in close collaboration with each other and with the other stakeholders. We identified ten benefits which included improved communication, transparency, dependency management and decision making. We identified nine challenges which included the lacking preparation and prioritization of requirements, unrealistic schedules, insufficient architectural planning and lacking agile mindset. The biggest improvements to the method were the introduction of frequent status checks and a big visible planning status board.ConclusionThe release planning method ameliorated many difficult characteristics of the release planning problem but its efficiency was negatively affected by the performing organization that was in transition from a plan-driven to an agile development mindset. Even in this case the benefits clearly outweighed the challenges and the method enabled the early identification of the issues in the project.     

Application of fuzzy expert systems in assessing operational risk of software

Risk is the potential for realization of undesirable consequences of an event. Operational risk of software is the likelihood of untoward events occurring during operations due to software failures. NASA IV&V Facility is an independent institution which conducts Independent Assessments for various NASA projects. Its responsibilities, among others, include the assessments of operational risks of software. In this study, we investigate Independent Assessments that are conducted very early in the software development life cycle.Existing risk assessment methods are largely based on checklists and analysis of a risk matrix, in which risk factors are scored according to their influence on the potential operational risk. These scores are then arithmetically aggregated into an overall risk score. However, only incomplete project information is available during the very early phases of the software life cycle, and thus, a quantitative method, such as a risk matrix, must make arbitrary assumptions to assess operational risk.We have developed a fuzzy expert system, called the Research Prototype Early Assessment System, to support Independent Assessments of projects during the very early phases of the software life cycle. Fuzzy logic provides a convenient way to represent linguistic variables, subjective probability, and ordinal categories. To represent risk, subjective probability is a better way than quantitative objective probability of failure. Furthermore, fuzzy severity categories are more credible than numeric scores. We illustrated how fuzzy expert systems can infer useful results by using the limited facts about a current project, and rules about software development. This approach can be extended to add planned IV&V level, history of past NASA projects, and rules from NASA experts.     

Intangible benefits valuation in ERP projects

The development, implementation and ownership of information systems, especially large‐scale systems such as enterprise resource planning (ERP), has become progressively longer in duration and more cost intensive. As a result, IS managers are being required to justify projects financially based on their return. Historically, information systems have been difficult to quantify in monetary terms because of the intangible nature of many of the derived benefits, e.g. improved customer service. Using the case study methodology, this paper examines an attempt by a large computer manufacturer to incorporate intangibles into traditional cost–benefit analysis in an ERP project. The paper reviews the importance of intangibles, lists intangible benefits that are important in ERP projects and demonstrates the use of a scheme through which they can be incorporated into traditional evaluation techniques.     

Functional networks as a novel data mining paradigm in forecasting software development efforts

This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regression, and case-based reasoning (CBR). Unfortunately, such forecasting models have numerous of drawbacks, namely, their inability to deal with uncertainties and imprecision present in software projects early in the development life-cycle. The main benefit of this study is to utilize both function points and development environments of recent software development cases prominent, which have high impact on the success of software development projects. Both implementation and learning process are briefly proposed. We investigate the efficiency of the new framework for predicting the software development efforts using both simulation and COCOMO real-life databases. Prediction accuracy of the functional networks framework is evaluated and compared with the commonly used regression and neural networks-based models. The results show that the new intelligence paradigm predicts the required efforts of the initial stage of software development with reliable performance and outperforms both regression and neural networks-based models.