Günther Ruhe

Software release planning: an evolutionary and iterative approach

Abstract To achieve higher flexibility and to better satisfy actual customer requirements, there is an increasing tendency to develop and deliver software in an incremental fashion. In adopting this process, requirements are delivered in releases and so a decision has to be made on which requirements should be delivered in which release. Three main considerations that need to be taken account of are the technical precedences inherent in the requirements, the typically conflicting priorities as determined by the representative stakeholders, as well as the balance between required and available effort. The technical precedence constraints relate to situations where one requirement cannot be implemented until another is completed or where one requirement is implemented in the same increment as another one. Stakeholder preferences may be based on the perceived value or urgency of delivered requirements to the different stakeholders involved. The technical priorities and individual stakeholder priorities may be in conflict and difficult to reconcile. This paper provides (i) a method for optimally allocating requirements to increments; (ii) a means of assessing and optimizing the degree to which the ordering conflicts with stakeholder priorities within technical precedence constraints; (iii) a means of balancing required and available resources for all increments; and (iv) an overall method called EVOLVE aimed at the continuous planning of incremental software development. The optimization method used is iterative and essentially based on a genetic algorithm. A set of the most promising candidate solutions is generated to support the final decision. The paper evaluates the proposed approach using a sample project.

The art and science of software release planning

Incremental development provides customers with parts of a system early, so they receive both a sense of value and an opportunity to provide feedback early in the process. Each system release is thus a collection of features that the customer values. Furthermore, each release serves to fix defects detected in former product variants. Release planning (RP) addresses decisions related to selecting and assigning features to create a sequence of consecutive product releases that satisfies important technical, resource, budget, and risk constraints.Release planning is an important and integral part of any incremental product development. It addresses decisions related to selecting and assigning features to a consecutive product releases such ...

Quantitative studies in software release planning under risk and resource constraints

Delivering software in an incremental fashion implicitly reduces many of the risks associated with delivering large software projects. However, adopting a process, where requirements are delivered in releases means decisions have to be made on which requirements should be delivered in which release. This paper describes a method called EVOLVE+, based on a genetic algorithm and aimed at the evolutionary planning of incremental software development. The method is initially evaluated using a sample project. The evaluation involves an investigation of the tradeoff relationship between risk and the overall benefit. The link to empirical research is two-fold: firstly, our model is based on interaction with industry and randomly generated data for effort and risk of requirements. The results achieved this way are the first step for a more comprehensive evaluation using real-world data. Secondly, we try to approach uncertainty of data by additional computational effort providing more insight into the problem solutions: (i) effort estimates are considered to be stochastic variables following a given probability function; (ii) instead of offering just one solution, the L-best (L > 1) solutions are determined. This provides support in finding the most appropriate solution, reflecting implicit preferences and constraints of the actual decision-maker. Stability intervals are given to indicate the validity of solutions and to allow the problem parameters to be changed without adversely affecting the optimality of the solution.

Trade-off analysis for requirements selection

Evaluation, prioritization and selection of candidate requirements are of tremendous importance and impact for subsequent software development. Eort, time as well as quality constraints have to be taken into account. Typically, dieren t stakeholders have conicting priorities and the requirements of all these stakeholders have to be balanced in an appropriate way to ensure maximum value of the nal set of requirements. Tradeo analysis is needed to proactively explore the impact of certain decisions in terms of all the criteria and constraints. The proposed method called Quantitative WinWin uses an evolutionary approach to provide support for requirements negotiations. The novelty of the presented idea is four-fold. Firstly, it iteratively uses the Analytical Hierarchy Process (AHP) for a stepwise analysis with the aim to balance the stakeholders’ preferences related to dieren t classes of requirements. Secondly, requirements selection is based on predicting and rebalancing its impact on eort, time and quality. Both prediction and rebalancing uses the simulation model prototype GENSIM. Thirdly, alternative solution sets oered for decision-making are developed incrementally based on thresholds for the degree of importance of requirements and heuristics to nd a best t to constraints. Finally, trade-o analysis is used to determine non-dominated extensions of the maximum value that is achievable under resource and quality constraints. As a main result, quantitative WinWin proposes a small number of possible sets of requirements from which the actual decision-maker can nally select the most appropriate solution.

Software Engineering Decision Support – A New Paradigm for Learning Software Organizations

Software development and evolution is characterized by multiple objectives and constraints, by a huge amount of uncertainty, incomplete information and changing problem parameters. Success of software development very much depends on providing the right knowledge at the right time, at the right place, and for the appropriate person. Experience factory and organizational learning approaches are increasingly used to improve software development practices.

Optimized Resource Allocation for Software Release Planning

Release planning for incremental software development assigns features to releases such that technical, resource, risk and budget constraints are met. Planning of software releases and allocation of resources cannot be handled in isolation. A feature can be offered as part of a release only if all its necessary tasks are done before the given release date. We assume a given pool of human resources with different degrees of productivity to perform different types of tasks. To address the inherent difficulty of this process, we propose a two-phased optimization approach that combines the strength of two existing solution methods. The industrial applicability of the approach is primarily directed towards mature organizations having systematic development and measurement processes in place. The expected practical benefit of the planning method is to provide release plan solutions that achieve a better overall business value (e.g., expressed by the degree of stakeholder satisfaction) by better allocation of resources. Without ignoring the importance of the human expert in this process, the contributions of the paper are seen in making the overall process more objective and the resulting decisions more transparent.

Decision Support in Requirements Engineering

Decisions are increasingly understood as the crystallization points of the software development process. Despite the abundance of the requirements engineering (RE) processes, little attention has been given to providing appropriate support for making RE decisions. In this chapter we analyze current research related to RE decision making. We study how and when decisions are made in RE and the underlying methodology. Our focus is not to provide solution approaches for particular decision problems in RE, but to discuss strategies for improving research and practice in the RE decision making process. We have performed an extensive analysis of related research. Our findings show the difficulties in RE decision making and the deficits of current research. We position decision support at the appropriate approach to handle incompleteness and uncertainty of information as is mostly the case in RE. Based on this, we propose an agenda for future research.

Evaluating the learning effectiveness of using simulations in software project management education: results from a twice replicated experiment

Abstract The increasing demand for software project managers in industry requires strategies for the development of management-related knowledge and skills of the current and future software workforce. Although several educational approaches help to develop the necessary skills in a university setting, few empirical studies are currently available to characterise and compare their effects. This paper presents the results of a twice replicated experiment that evaluates the learning effectiveness of using a process simulation model for educating computer science students in software project management. While the experimental group applied a System Dynamics simulation model, the control group used the well-known COCOMO model as a predictive tool for project planning. The results of each empirical study indicate that students using the simulation model gain a better understanding about typical behaviour patterns of software development projects. The combination of the results from the initial experiment and the two replications with meta-analysis techniques corroborates this finding. Additional analysis shows that the observed effect can mainly be attributed to the use of the simulation model in combination with a web-based role-play scenario. This finding is strongly supported by information gathered from the debriefing questionnaires of subjects in the experimental group. They consistently rated the simulation-based role-play scenario as a very useful approach for learning about issues in software project management.

Intelligent Support for Selection of COTS Products

Intelligent Decision Support is considered in unstructured decision situations characterized by one or more of the following factors: complexity, uncertainty, multiple groups with a stake in the decision outcome (multiple stakeholders), a large amount of information (especially company data), and/or rapid change in information. Support here means to provide access to information that would otherwise be unavailable or difficult to obtain; to facilitate generation and evaluation of solution alternatives, and to prioritize alternatives by using explicit models that provide structure for particular decisions. Integration of commercial off the shelf (COTS) products as elements of larger systems is a promising new paradigm. In this paper, we focus on the selection of COTS products. This is characterized as a problem with a high degree of inherent uncertainty, incompleteness of information, dynamic changes and involvement of conflicting stakeholder interests. A semi-formal problem description is given. We derive requirements on Decision Support Systems for COTS selection and discuss ten existing approaches from the perspective of those requirements. As a result, we propose an integrated framework called COTS-DSS combining techniques and tools from knowledge management and artificial intelligence, simulation and decision analysis.

Intelligent Support for Software Release Planning

One of the most prominent issues involved in incremental software development is to decide upon the most appropriate software release plans taking into account all explicit and implicit objectives and constraints. Such decisions have become even more complicated in the presence of large number of stakeholders such as different groups of users, managers, or developers. However, early involvement of customers and understanding of their real needs is one of the core success factors of software business [16]. This paper introduces a six step process model for release planning. It is inspired by the Quality Improvement Paradigm [2], as release planning is a learning and improvement process as well. Emphasis is on proposing the tool support implementing this process. The use of the intelligent decision support tool ReleasePlanner is presented by comparing a baseline scenario reflecting current state-of-the practice of release planning with a supposed improvement scenario obtained after usage of the tool. Initial experience from a real-world environment at iGrafx Corel Inc. is used to validate the improvement scenario.