Measuring dependency constraint satisfaction in software release planning using dissimilarity of fuzzy graphs

Software Release Planning (SRP) is to find, for a software, a subset of the requirements with the highest value while respecting the budget. The value of a requirement however may, to various degrees, depend on selecting or ignoring other requirements. However, existing SRP models ignore either Value-Related Dependencies altogether or the strengths of those dependencies even if they consider them. This paper presents an Integer Programming model for software release planning, which considers the variances of strengths of positive and negative value-related dependencies among software requirements. To capture the imprecision associated with strengths of value-related dependencies we have made use of algebraic structure of fuzzy graphs. We have further, contributed a scalable technique for automated identification of value-related dependencies based on user preferences for software requirements. The validity of the work is verified through simulations.

Software release planning is the process of deciding what to include in future release(s) of a product. Basically the problem can be seen as a company-wide optimization problem involving many stakeholders where the goal is to maximize utilization of the often limited resources of a company and turn them into business benefit. Saliu and Ruhe have proposed a set of key aspects for release planning methods, of which only a subset have been validated in industry. In this paper we use the Saliu and Ruhe key aspects as a starting point for identifying key aspects of release planning. To do this we have performed a multiple case study involving 7 international industrial companies, all producers of software intensive products. Our contribution is 1) a more strict meaning of a release planning key aspect, 2) validation of some of the aspects proposed by Saliu and Ruhe, and 3) an extension of the key aspects. We also capture state-of-the-practice for release planning in industry.

BackgroundRelease planning (RP) is one of the most complex and relevant activities in the iterative and incremental software development, because it addresses all decisions associated with the selection and assignment of requirements to releases. There are many approaches in which RP is formalized as an optimization problem. In this context, search-based software engineering (SBSE) deals with the application of search techniques to solve complex problems of software engineering. Since RP is a wicked problem with a large focus on human intuition, the decision maker’s (DM) opinion is a relevant issue to be considered when solving release planning problem. Thus, we emphasize the importance in gathering the DM’s preferences to guide the optimization process through search space area of his/her interests.MethodsTypically, RP is modelled as a multi-objective problem by considering to maximize overall clients satisfaction and minimize project risk. In this paper, we extend this notion and consider DM’s preferences as an additional objective. The DM defines a set of preferences about the requirements allocation which is stored in a preference base responsible for influencing the search process. The approach was validated through an empirical study, which consists of two different experiments, respectively identified as (a) automatic experiment and (b) participant-based experiment. Basically, the former aims to analyze the approach using different search-based algorithms (NSGA-II, MOCell, IBEA, and SPEA-II), over artificial and real-world instances, whereas the latter aims at evaluating the use of the proposal in a real scenario composed of human evaluations.ResultsThe automatic experiment points out that NSGA-II obtained overall superiority in two of the three datasets investigated, positioning itself as a superior search technique for scenarios with few number of requirements and preferences, while IBEA showed to be better for larger ones (with more requirements and preferences). Regarding the participant-based experiment, it was found that two thirds of the participants evaluated the preference-based solution better than the non-preference-based one.ConclusionsThe results suggest that it is feasible to investigate the approach in a real-world scenario. In addition, we made available a prototype tool in order to incorporate the human’s preferences about the requirements allocation into the solution of release planning.

Release planning for incremental software development assigns features to releases such that most important technical, resource, risk and budget constraints are met. The research presented in this paper is based on a three staged procedure. In addition to an existing method for (i) strategic release planning that maps requirements to subsequent releases and (ii) a more fine-grained planning that defines resource allocations for each individual release, we propose a third step, i.e., (iii) stability analysis, which analyzes proposed release plans with regards to their sensitivity to unforeseen changes. Unforeseen changes can relate to alterations in expected personnel availability and productivity, feature-specific task size (measured in terms of effort), and degree of task dependency (measured in terms of work load that can only be processed if corresponding work in predecessor tasks has been completed). The focus of this paper is on stability analysis of proposed release plans. We present the simulation model REPSIM (Release Plan Simulator) and illustrate its usefulness for stability analysis with the help of a case example.

Numerous factors are involved when deciding when to implement which features in incremental software development. To facilitate a rational and efficient planning process, release planning models make such factors explicit and compute release plan alternatives according to optimization principles. However, experience suggests that industrial use of such models is limited. To investigate the feasibility of model and tool support, we compared input factors assumed by release planning models with factors considered by expert planners. The former factors were cataloged by systematically surveying release planning models, while the latter were elicited through repertory grid interviews in three software organizations. The findings indicate a substantial overlap between the two approaches. However, a detailed analysis reveals that models focus on only select parts of a possibly larger space of relevant planning factors. Three concrete areas of mismatch were identified: (1) continuously evolving requirements and specifications, (2) continuously changing prioritization criteria, and (3) authority-based decision processes. With these results in mind, models, tools and guidelines can be adjusted to address better real-life development processes.

Usability is one of the most important aspects of software. A multitude of methods and techniques intended to support the development of usable systems has been provided, but the impact on industrial software development has been limited. One of the reasons for this limited success is the gap between traditional academic theory generation and commercial practice. Another reason is the gap between usability engineering and established requirements engineering practice. This thesis is based on empirical research and puts a usability focus on three important aspects of requirements engineering: elicitation, specification and release planning. There are two main themes of investigation. The first is concerned with the development and introduction of a usability-oriented method for elicitation and specification of requirements, with an explicit focus on utilizing the skills of technical communicators. This longitudinal, qualitative study, performed in an industrial setting in the first half of the nineties, provides ample evidence in favor of a closer collaboration between technical communicators and system developers. It also provides support for the benefits of a task-oriented approach to requirements elicitation. The results are also reflected upon in a retrospective paper, and the experiences point in the direction of an increased focus on the specification part, in order to bridge the gap between usability engineering and established requirements management practice. The second represents a usability-oriented approach to understanding and supporting release planning in software product development. Release planning is an increasingly important part of requirements engineering, and it is complicated by intricate dependencies between requirements. A survey performed at five different companies gave an understanding of the nature and frequency of these interdependencies. This knowledge was then turned into the design and implementation of a support tool, with the purpose of provoking a deeper understanding of the release planning task. This was done through a series of cooperative evaluation sessions with release planning experts. The results indicate that, although the tool was considered useful by the experts, the initial understanding of the task was overly simplistic. As a result, a number of design implications are proposed.

Many present-day microprocessors have fine grain parallelism, be it in the form of a pipeline, of multiple functional units, or replicated processors. The efficient use of such architectures depends on the capability of the compiler to schedule the execution of the object code in such a way that most of the available hardware is in use while complying with data dependences. In the case of one simple loop, the schedule may be expressed as an affine form in the loop counter. The coefficient of the loop counter in the schedule is the initiation interval, and gives the mean rate at which loop bodies may be executed. The dependence constraints may be converted to linear inequalities in the coefficients of a closed form schedule, and then solved by classical linear programming algorithms. The resource constraints, however, translate to non-linear constraints. These constraints become linear if the initiation interval is known. This leads to a fast searching algorithm, in which the initiation interval is increased until a feasible solution is found.

Software release planning is a challenge task as it includes different stakeholder perspectives, competing objectives, and different types of constraints. Additionally, most of information is used during the process of software release planning usually uncertain. Last few years, significant progress has been made in software release planning research. Many software release planning (SRP) models have been proposed to address challenges associated with software release planning problem. Comparing SRP models with respect to these challenges will help in identifying which challenges are not or partially addressed. These challenges need to be studied by researchers. In this paper, a comparative study is proposed. In the proposed study, SRP problem and solution spaces are explored, a number of comparison criteria representing key challenges associated with SRP problem are identified, 22 well-known SRP models are identified and compared according to 11 comparison criteria using Weighted Score Method (WSM). Results of the comparative study identify a set of challenges that are not or partially addressed and need more research and investigation.

Search-based software engineering (SBSE) consists of the application of metaheuristic search techniques—such as genetic algorithms, simulated annealing, hill climbing, and tabu search—to software engineering problems. This idea is based on the observation that some software engineering activities can be thought of as optimization problems, and on the fact that techniques providing exact solutions are, very often, unable to adequately scale to large, real-world software engineering problems. Define fitness functions to guide an automated search for solutions, rather than attempting to construct solutions from scratch by hand. SBSE moves the engineer up the abstraction chain, from the tedious and expensive role of detailed solution construction, to the potentially more productive and more cost-effective area of fitness function design. This approach has been repeatedly demonstrated to have successful applications across the software engineering life cycle from requirements and project planning to maintenance and testing. This special issue focuses on applications of search SBSE to the maintenance phase of the life cycle. After software testing 2, software maintenance is the most widely studied application area for SBSE research. Work on SBSE applications to problems in software maintenance can be traced back to the pioneering work of the Mancoridis et al. 3, which led to the development of the Bunch tool for software re-modularization 4. In the past five years there has been a significant increase in SBSE applications to software maintenance and evolution. Search-based techniques have been applied to post-development code improvement problems, such as object-oriented refactoring 5, 6, and imperative language slicing and transformation 7. Other applications are related to software engineering economics and project management problems, such as release planning 8-12, effort, and cost estimation 13-16, and project staffing and planning 17-20. Last, but not the least, SBSE has been applied to deal with service-oriented architectures, for example for services QoS-aware composition 21, service-level agreement negotiation 22, and testing 23. A complete overview of SBSE with a discussion of future research trends can be found in the recent International Conference on Software Engineering (ICSE) ‘Future of Software Engineering’ paper 24. This issue of the Journal of Software Maintenance and Evolution: Research and Practice features three outstanding papers, dealing with different aspects of software maintenance and evolution: release planning and staffing, re-factoring, and component substitution. The paper ‘Optimized Staffing for Product Releases—Focused Search and its Application at Chartwell Technologies’ by Puneet Kapur, An Ngo-The, Günther Ruhe, and Andrew Smith presents an approach for optimizing the staffing for product releases. The problem they solve can be viewed as part of the more general release planning problem, which assigns features to releases such that technical, resource, risk, and budget constraints are met. In particular, the genetic search-based approach the authors propose aims at assigning human resources to software development tasks, taking them from a pool of developers having different levels of skills. The applicability of the proposed approach is demonstrated with an industrial case study. The paper ‘Search-Based Refactoring: an Empirical Study’ by Mark O'Keffe and Mel Ó Cinnéide presents an approach to automatically re-factor, using search-based techniques, object-oriented systems that undergo repeated addition of functionality. Such addition can, very often, deteriorate the quality of the system's underlying design. Their re-factoring is guided by the QMOOD quality model 25, and the paper compares different optimization techniques, such as Genetic Algorithms, Simulated Annealing, and Multiple Ascent Hill-Climbing. The results of experiments carried out on five software systems suggest that the latter technique outperforms the others. The paper ‘Search-based Many-to-one Component Substitution’ by Nicolas Desnos, Marianne Huchard, Guy Tremblay, Christelle Urtado, and Sylvain Vauttier presents a search-based approach that allows the replacement of an obsolete component with a composition of multiple components. When a component becomes obsolete, the approach searches for a minimal assembly of other components, exposing their functionality by means of ports that preserves the behavior of the original component. The proposed approach has been evaluated on a set of randomly generated components. We hope that readers will enjoy this special issue and find the selected papers insightful and inspiring. We would like to thank all the authors who presented papers for this special issue: there were many good quality papers, but unfortunately we could accept only three of them. Last, but not the least, we would like to thank the reviewers who provided timely, detailed and constructive feedbacks to the authors. Without their precious help this special issue would not have been possible.

The increasing complexity of software development has prompted a shift in project management practices toward agile methodologies, often supported by specialized software tools for planning and reporting. However, many widely adopted tools offer only limited decision support capabilities. This work introduces ProjectION, an intelligent software tool designed to enhance decision-making in agile project management through techniques relative to the Search-Based Software Engineering field. Based on an in-depth analysis of the challenges faced in agile environments, ProjectION assists decision makers in monitoring project status, forecasting progress, and automating software release planning. A comprehensive usability study was conducted with a small sample of experienced IT professionals, following the ISO/IEC 25062:2006 Common Industry Format. Key usability metrics of effectiveness, efficiency, and user satisfaction were assessed. Despite minor usability issues, results indicate that ProjectION effectively supports agile release planning. A second experiment further demonstrates the tool’s utility in generating optimal release plans, outperforming manual solutions proposed by decision makers. To foster collaboration and future development, the core algorithms and execution service have been made publicly available, enabling integration of novel approaches to the Next Release Problem.

Software release planning (SRP) is the problem of selecting which features or requirements will be included in the next release or releases. It is a crucial step in software development, which happens to be extremely complex given the need to reconcile multiple decision making criteria, (e.g., business value, effort and cost), while considering several constraints (e.g., feature precedencies, resource availability). For this reason, several SRP models have been proposed in the literature. The objective of this paper is to provide an updated review of SRP approaches reported in the literature.

Software release planning: an evolutionary and iterative approach

Task assignment optimization in geographically distributed data centers

In this chapter we address the problem of mapping a biochemical application modeled as a sequencing graph to a given biochip architecture. The mapping consists of the binding, scheduling, and routing tasks. We first propose a constraint programming (CP) synthesis approach that optimally synthesizes a biochemical application onto the specified biochip architecture with the application completion time minimization as the target objective. The synthesis process involves performing binding and scheduling of operations while satisfying the dependency and resource constraints. Our CP approach ignores fluid routing. We also propose a computationally efficient heuristic approach that also takes into account fluidic routing (contention aware edge scheduling) together with the operation mapping. Real-life case studies and a set of synthetic benchmarks have been synthesized on different architectures for validating the proposed approach. To aid the development and testing during the application development phase, we have also developed a simulator that shows how a given mapped biochemical application is executed on a target biochip architecture. The proposed approaches are expected to reduce human effort, enabling designers to take early design decisions by being able to evaluate their proposed architecture, minimizing the design cycle time and also facilitating programmability and automation.

Since there is generally insufficient instruction level parallelism within a single basic block, higher performance is achieved by speculatively scheduling operations in superblocks. This is difficult in general because each branch competes for the processor's limited resources. Previous work manages the performance tradeoffs that exist between branches only indirectly. We show here that dependence and resource constraints can be used to gather explicit knowledge about scheduling tradeoffs between branches. The first contribution of this paper is a set of new, tighter lower bounds on the execution times of superblocks that specifically accounts for the dependence and resource conflicts between pairs of branches. The second contribution of this paper is a novel superblock scheduling heuristic that finds high performance schedules by determining the operations that each branch needs to be scheduled early and selecting branches with compatible needs that favor beneficial branch tradeoffs. Performance evaluations for superblocks from SPECint95 indicate that our bounds are very tight and that our scheduling heuristic outperforms well known superblock scheduling algorithms.