An integrated approach for requirement selection and scheduling in software release planning

It is essential for product software companies to decide which requirements should be included in the next release and to make an appropriate time plan of the development project. Compared to the extensive research done on requirement selection, very little research has been performed on time scheduling. In this paper, we introduce two integer linear programming models that integrate time scheduling into software release planning. Given the resource and precedence constraints, our first model provides a schedule for developing the requirements such that the project duration is minimized. Our second model combines requirement selection and scheduling, so that it not only maximizes revenues but also simultaneously calculates an on-time-delivery project schedule. Since requirement dependencies are essential for scheduling the development process, we present a more detailed analysis of these dependencies. Furthermore, we present two mechanisms that facilitate dynamic adaptation for over-estimation or under-estimation of revenues or processing time, one of which includes the Scrum methodology. Finally, several simulations based on real-life data are performed. The results of these simulations indicate that requirement dependency can significantly influence the requirement selection and the corresponding project plan. Moreover, the model for combined requirement selection and scheduling outperforms the sequential selection and scheduling approach in terms of efficiency and on-time delivery. \u

Validated Software Cost Estimation Factors for Government Projects using Rasch Measurement Model

Software cost estimation (SCE) in software management can be a complicated task, as it could yield inaccurate results. Based on new empirical evidence, Public sectors more often face estimation failure, which causes projects to over shoot budgets, get delayed, face termination or the project scope or requirement to remain incomplete. Hence, the main aim of this paper is to identify the critical factors that significantly impact SCE in the context of software development in the Indonesian regional government. This research employs a quantitative approach, in which a questionnaire is used as the data collection instrument. The data is analysed using a RASCH model. This study is conducted in the regional government of West Sumatera Province, Indonesia. The result of the study reveals that there are six critical factors that significantly impact SCE results in a government project. These critical factors are programmer capability, top management support, the understanding of top management regarding the objectives of the project, risk management, knowledge, competency of the project manager, and top management involvement in the project

Software Effort Estimation as Collective Accomplishment: An analysis of estimation practice in a multi-specialist team

This paper examines how a team of software professionals goes about estimating the effort of a software project using a judgment-based, bottom-up estimation approach. By employing a social practice perspective that highlights the distributed character of expertise and conceives actions as mediated by cultural tools, the paper analyzes the interactional process through which the estimation tasks were collectively accomplished. The findings show how software effort estimation is carried out through complex series of explorative and sense-making actions, rather than by applying assumed information or routines. During the explorative work, the team alternated between the planning and the problem solving aspects of the activity. The requirement specification served several mediating functions in the interactional process, through which expertise was mobilised and coordinated. The paper argues that to grasp the complexity of software estimation, there is a need for more research that accounts for the communicative and interactional dimensions of this activity. Moreover, by revealing the interactional details of a planning activity the paper contributes to our understanding of the future-oriented and constructive dimensions of social practices

Predictiveness and Effectiveness of Story Points in Agile Software Development

Agile Software Development (ASD) is one of the most popular iterative software development methodologies, which takes a different approach from the conventional sequential methods. Agile methods promise a faster response to unanticipated changes during development, typically contrasted with traditional project development, which assumes that software is specifiable and predictable. Traditionally, practitioners and researchers have utilised different Functional Size Measures (FSMs) as the main cost driver to estimate the effort required to develop a project (Software Effort Estimation – SSE). However, FSM methods are not easy to use with ASD. Thus, another measure, namely Story Point (SP), has become popular in this context. SP is a relative unit representing an intuitive mixture of complexity and the required effort of a user requirement. Although recent surveys report on a growing trend toward intelligent effort estimation techniques for ASD, the adoption of these techniques is still limited in practice. Several factors limit the accuracy and adaptability of these techniques. The primary factor is the lack of enough noise-free information at the estimation time, restricting the model’s accuracy and reliability. This thesis concentrates on SEE for ASD from both the technique and data perspectives. Under this umbrella, I first evaluate two prominent state-of-the-art works for SP estimation to understand their strengths and weaknesses. I then introduce and evaluate a novel method for SP estimation based on text clustering. Next, I investigate the relationship between SP and development time by conducting a thorough empirical study. Finally, I explore the effectiveness of SP estimation methods when used to estimate the actual time. To carry out this research, I have curated the TAWOS (Tawosi Agile Web-based Open-Source) dataset, which consists of over half a million issues from Agile, open-source projects. TAWOS has been made publicly available to allow for reproduction and extension in future work

Requirements Prioritization Based on Benefit and Cost Prediction: An Agenda for Future Research

In early phases of the software cycle, requirements prioritization necessarily relies on the specified requirements and on predictions of benefit and cost of individual requirements. This paper presents results of a systematic review of literature, which investigates how existing methods approach the problem of requirements prioritization based on benefit and cost. From this review, it derives a set of under-researched issues which warrant future efforts and sketches an agenda for future research in this area

Root Cause Analysis in Business Processes

Conceptual modeling is an important tool for understanding and revealing weaknesses of business processes. Yet, the current practice in reengineering projects often considers simply the as-is control flow and uses the respective model barely as a reference for brain-storming about improvement opportunities. This approach heavily relies on the intuition of the participants and misses a clear description of steps to identify root causes of problems. In contrast to that, this paper introduces a systematic methodology to detect and document the quality dimension of a business process. It builds on the definition of softgoals for each process activity, of correlations between softgoals, and metrics to measure the occurrence of quality issues. In this regard our contribution is a foundation of root-cause analysis in business process modeling, and a conceptual integration of goal-based and activity-based approaches to capturing processes

Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction