Simulation-Based Stability Analysis for Software Release Plans

Release planning is a cornerstone problem in incremental software development. It deals with the assignment of requirements to sequence of releases in order to maximize profit, minimize the delay of feedback and return of investment in such a way that dependency and resource constraints are met. Release planning decisions are required at an early stage in the development cycle, when uncertainty is unavoidable in the project estimates. Recently, there are some works concerning the use of fuzzy theory to address issues concerning the uncertainty in the release planning problem: fuzzy effort constraints and fuzzy dependency constraints. In this paper, we study the application of fuzzy theory to handle the uncertainty concerning dependency constraints from a holistic perspective, i.e. the whole set of fuzzy dependency constraints is considered as a fuzzy graph. The satisfaction of dependency constraints in a solution plan is measured by the distance between this plan and an ideal plan (in terms of the dependency constraints). The distance is materialized as the distance between two fuzzy graphs. This is considered to be an essential support for the actual decision-making. All the concepts and the complete approach are illustrated by a case study example.

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.

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.

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 is of key importance for incremental software product development. With the increasing size and complexity of software products, as well as with the growing demands for transparency and objectivity of decision-making, intuition alone is no longer sufficient for release planning. EVOLVE+ is a systematic method for release planning, which combines the strengths of formalization and computational efficiency with the expertise of the human experts. From a given set of candidate features, the most attractive ones are selected and assigned to a sequence of releases. In this paper, we consider three extensions to the current systematic planning approach EVOLVE+: (a) value functions describing the estimated value of the feature are continuous functions of time, (b) the actual release dates are no longer fixed but can be varied in some pre-defined interval. As a consequence, the available resource capacities are also functions of time. (c) Calculation of trade-off solutions balancing the risk with the potential additional value of early release. All three extensions substantially increase the complexity of the release planning problem. We have developed a solution method using genetic algorithms that is able to accommodate the additional complexity of the advanced model. A hypothetical case study is conducted as a proof-of-concept for the applicability of the method. The applicability of the method is demonstrated by analyzing six detailed planning scenarios.

Abstract— This study proposes the iterative Simulation‐Based Release (ISR) policy for color‐filter (CF) fabs based on iterative simulation. In the studied CF fabs, multi‐domain vertical‐alignment (MVA) equipment has the capability and flexibility to provide additional production capacity of black‐matrix (BM) and red, green, and blue (RGB) layers. Fab throughput can be increased by setting up MVA equipment to process BM and RGB layers when its available production capacity is larger than the scheduled manufacturing target. An AutoMod simulation model was developed and validated based on a real CF fab in Taiwan. A heuristic lot release plan was developed and used as the input of simulation. A better release plan can be developed by taking advantage of the MVA's line idle periods over the planning horizon generated from simulation and releasing new lots to the MVA line to increase its throughput. Release plans can be modified iteratively when the simulation results of the previous release plans show sufficient idle periods for the MVA line operation. The case study demonstrated that the proposed ISR is effective in idle‐time reduction (more than 60%) and throughput improvement (more than 2% in following months).

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 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.

In market-driven product development, it is important that the software product is released to the market at the right time, and offers higher quality than the competitors. In release planning, the allocation of development effort in investments into product enhancements, functions are competing with quality requirements for limited resources. In addition, it is important to find the right balance between competing quality requirements. In this paper, we present an industrial evaluation of the benefit and cost views in the QUality PERformance (QUPER) model. The results indicate that the model is relevant in the release planning process, and that the combination of the benefit and cost views provides a clear picture of the current market situation.

Decisions about the release date need to balance between the degree of readiness (quality) of the product and the potential competitive advantage and added value of (early) delivery. Based on an existing optimization approach for solving the maximum value release planning problem for a fixed release time, we provide a re-optimization approach for which includes local and global re-planning exchange operations to determine the outcome of varying the release dates. The proposed method determines a set of trade-off solutions related to (i) the value of the features implemented, (ii) their estimated quality, and (iii) the release time. The approach is evaluated by a real-world case study taken from an ongoing project which is to develop the new main release 2.0 for the release planning decision support platform ReleasePlanner™ v1.6.

Software release planning: an evolutionary and iterative approach

The use of Commercial Off‐The‐Shelf (COTS) products in the software development process requires the evaluation of existing COTS products, and then selecting the one that best fits system requirements. In this process, it is inevitable to encounter mismatches between COTS features and system requirements. Mismatches occur as a result of an excess or shortage of COTS capabilities. Many of these mismatches are resolved after selecting a COTS product. Existing COTS‐selection approaches fail to properly consider these mismatches. This article presents MiHOS (Mismatch Handling for COTS Selection), an approach that aims at addressing mismatches while considering limited resources. MiHOS can be integrated with existing COTS‐selection methods at two points: (i)When evaluating COTS candidates in order to estimate the anticipated fitness of the candidates if their mismatches are resolved. This helps to base our COTS‐selection decisions on the fitness that the COTS candidates will eventually have if selected. (ii) After selecting a COTS product in order to plan the resolution of the most appropriate mismatches using suitable actions, such that the most important risk, technical, and resource constraints are met. A case study from the e‐services domain is used to illustrate the method and to discuss its added value. Copyright © 2008 John Wiley & Sons, Ltd.

Uncertainty quantification of structural and geotechnical parameter by geostatistical simulations applied to a stability analysis case study with limited exploration data

This paper deals with the soil slope stability analysis along the road section (National Highway-05) near Luhri area, Himachal Pradesh, India. The area constantly experiences local as well as regional slides which enhances the siltation, reduce carrying capacity of the Sutlej river with time and further increases the water level on the banks of the slope. High traffic congestion puts the area at high risk zone and therefore, stability analysis was done using probabilistic as well as deterministic approach under static conditions through limit equilibrium method (LEM) and finite element method (FEM). FEM was used for stability analysis, mainly to obtain deformational characteristics, under static conditions and compared with LEM results. The results of both the methods help in identifying the zones of vulnerability in the case study. Further, the values of safety factor obtained from both the methods are in close approximation. The study also reveals that the slope is critically stable and a remedial measure has been suggested by modifying the geometry of the slope, which needs proper protection from any external factors like rainfall and earthquakes that may cause unforeseeable circumstances.