This paper introduces app store mining and analysis as a form of software repository mining. Unlike other software repositories traditionally used in MSR work, app stores usually do not provide source code. However, they do provide a wealth of other information in the form of pricing and customer reviews. Therefore, we use data mining to extract feature information, which we then combine with more readily available information to analyse apps’ technical, customer and business aspects. We applied our approach to the 32,108 non-zero priced apps available in the Blackberry app store in September 2011. Our results show that there is a strong correlation between customer rating and the rank of app downloads, though perhaps surprisingly, there is no correlation between price and downloads, nor between price and rating. More importantly, we show that these correlation findings carry over to (and are even occasionally enhanced within) the space of data mined app features, providing evidence that our ‘App store MSR’ approach can be valuable to app developers

Context
Requirements optimization has been widely studied in the Search Based Software Engineering (SBSE) literature. However, previous approaches have not handled requirement interactions, such as the dependencies that may exist between requirements, and, or, precedence, cost- and value-based constraints.
Objective
To introduce and evaluate a Multi-Objective Search Based Requirements Selection technique, using chromosome repair and to evaluate it on both synthetic and real world data sets, in order to assess its effectiveness and scalability. The paper extends and improves upon our previous conference paper on requirements interaction management.
Method
The popular multi-objective evolutionary algorithm NSGA-II was used to produce baseline data for each data set in order to determine how many solutions on the Pareto front fail to meet five different requirement interaction constraints. The results for this baseline data are compared to those obtained using the archive based approach previously studied and the repair based approach introduced in this paper.
Results
The repair based approach was found to produce more solutions on the Pareto front and better convergence and diversity of results than the previously studied NSGA-II and archive-based NSGA-II approaches based on Kruskal–Wallis test in most cases. The repair based approach was also found to scale almost as well as the previous approach.
Conclusion
There is evidence to indicate that the repair based algorithm introduced in this paper is a suitable technique for extending previous work on requirements optimization to handle the requirement interaction constraints inherent in requirement interactions arising from dependencies, and, or, precedence, cost- and value-based constraints.

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE life cycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.
This paper provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

One important issue addressed by software companies is to determine which features should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while entailing the minimum cost for the company. This problem is known as the Next Release Problem (NRP). Since minimizing the total cost of including new features into a software package and maximizing the total satisfaction of customers are contradictory objectives, the problem has a multi-objective nature. In this work, we apply three state-of-the-art multi-objective metaheuristics (two genetic algorithms, NSGA-II and MOCell, and one evolutionary strategy, PAES) for solving NRP. Our goal is twofold: on the one hand, we are interested in analyzing the results obtained by these metaheuristics over a benchmark composed of six academic problems plus a real world data set provided by Motorola; on the other hand, we want to provide insight about the solution to the problem. The obtained results show three different kinds of conclusions: NSGA-II is the technique computing the highest number of optimal solutions, MOCell provides the product manager with the widest range of different solutions, and PAES is the fastest technique (but with the least accurate results). Furthermore, we have observed that the best solutions found so far are composed of a high percentage of low-cost requirements and of those requirements that produce the largest satisfaction on the customers as well.

Context In requirements engineering, there will be many different stake holders. Often the requirements engineer has to find a set of requirements that reflect the needs of several different stake holders, while remaining within budget. Objective This paper introduces an optimisation-based approach to the automated analysis of requirements assignments when multiple stake holders are to be satisfied by a single choice of requirements. Method The paper reports on experiments using two different multi-objective evolutionary optimisation algorithms with real world data sets as well as synthetic data sets. This empirical validation includes a statistical analysis of the performance of the two algorithms. Results The results reveal that the Two-Archive algorithm outperformed the others in convergence as the scale of problems increase. The paper also shows how both traditional and animated Kiviat diagrams can be used to visualise the tensions between the stake holders?competing requirements in the presence of increasing budgetary pressure. Conclusion This paper presented the concept of internal tensioning among multi-stakeholder in requirements analysis and optimisation for the first time. This analysis may be useful in internal negotiations over budgetary allowance for the project.

Requirements optimization has been widely studied in the SBSE literature. However, previous approaches have not handled requirements interactions, such as the dependencies that may exist between requirements, and, or, precedence, cost?and value–based constraints. To introduce and evaluate a Multi-Objective Search Based Requirements Selection technique, using chromosome repair and to evaluate it on both synthetic and real world data sets, in order to assess its effectiveness and scalability. The paper extends and improves upon our previous conference paper on Requirements Interaction Management. The popular Multi-Objective Evolutionary Algorithm NSGA-II was used to produce baseline data for each data set in order to determine how many solutions on the Pareto front fail to meet ?ve different requirement interaction constraints. The results for this baseline data are compared to those obtained using the Archive–based approach previously studied and the repair–based approach introduced in this paper. The repair–based approach was found to produce more points on the Pareto front and a better spread of results than the previously studied Archive-based approach. The repair–based approach was also found to scale almost as well as the previous approach. There is evidence to indicate that the repair–based algorithm introduced in this paper is a suitable technique for extending previous work on Requirements Optimization to handle the requirement interaction constraints inherent in requirement interactions arising from dependencies, and, or, precedence, cost?and value–based constraints.

Most software product developments are iterative and incremental processes that are seldom completed in a single release. It is critical but challenging to select the requirements from a large number of candidates for the achievement of overall busi- ness goal and the goals of multiple stakeholders, each of whom may have competing and often conflicting priorities. This thesis argues that search-based techniques can be applied to the optimisation problem during the requirements selection and analysis phase for release planning problem. Search-based techniques offer significant advantages; they can be used to seek robust, scalable solutions, to investigate trade-offs, to yield insight and to provide feedback explaining choices to the decision maker. In the thesis, a Search-based Requirements Selection and Optimisation Framework is proposed that includes search spaces, representations, solution processes and em- pirical studies. This framework formulates the requirements selection problem as an optimisation problem and allows multi-objective search-based techniques to be used in order to provide optimal or near optimal solutions and to find a suitable balance between priorities in different contexts. The thesis reports the results of experiments using different multi-objective evo- lutionary optimisation algorithms with real world data sets as well as synthetic data sets in three studies of the applications of this framework: Value/Cost Trade- off in Requirements Selection, Requirements Interaction Management and Multi- Stakeholder Requirements Analysis and Optimisation. Empirical validation includes a statistical analysis of the performance of the algorithms as well as simple graph- ical methods to visualise the discovered solutions in the multi-dimensional solution space. Though these visualisations are not novel in themselves, the thesis is the first to use them for visualisation of requirements optimisation spaces.

SBSE techniques have been widely applied to requirements selection and prioritization problems in order to ascertain a suitable set of requirements for the next release of a system. Unfortunately, it has been widely observed that requirements tend to be changed as the development process proceeds and what is suitable for today, may not serve well into the future. Though SBSE has been widely applied to requirements analysis, there has been no previous work that seeks to balance the requirements needs of today with those of the future. This paper addresses this problem. It introduces a multi-objective formulation of the problem which is implemented using multi-objective Pareto optimal evolutionary algorithms. The paper presents the results of experiments on both synthetic and real world data.

There has been much recent interest in Search Based Optimization for Requirements Selection from the SBSE community, demonstrating how multi-objective techniques can effectively balance the competing cost and value objectives inherent in requirements selection. This problem is known as release planning (aka the `next release problem). However, little previous work has considered the problem of Requirement Interaction Management (RIM) in the solution space. Because of RIM, there are many subtle relationships between requirements, which make the problem more complex than an unconstrained feature subset selection problem. This paper introduces and evaluates archive-based multi-objective evolutionary algorithm, based on NSGA-II, which is capable of maintaining solution quality and diversity, while respecting the constraints imposed by RIM.

In the past five years there has been a dramatic increase in work on Search Based Software Engineering (SBSE), an approach to software engineering in which search based optimisation algorithms are used to address problems in Software Engineering. SBSE has been applied to problems throughout the Software Engineering lifecycle, from requirements and project planning to maintenance and re-engineering. The approach is attractive because it offers a suite of adaptive automated and semi-automated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This paper provides a review and classification of literature on SBSE. The paper identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.

This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space. The paper also reports on experiments to determine the most suitable algorithm for this problem, comparing the results of the NSGA-II algorithms, a widely used multi objective evolutionary algorithm, and the Two-Archive evolutionary algorithm, a recently proposed alternative.

One of the first issues which has to be taken into account by software companies is to determine what should be included in the next release of their products, in such a way that the highest possible number of customers get satisfied while this entails a minimum cost for the company. This problem is known as the Next Release Problem (NRP). Since minimizing the total cost of including new features into a software package and maximizing the total satisfaction of customers are contradictory objectives, the problem has a multi-objective nature. In this work we study the NRP problem from the multi-objective point of view, paying attention to the quality of the obtained solutions, the number of solutions, the range of solutions covered by these fronts, and the number of optimal solutions obtained.Also, we evaluate the performance of two state-of-the-art multi-objective metaheuristics for solving NRP: NSGA-II and MOCell. The obtained results show that MOCell outperforms NSGA-II in terms of the range of solutions covered, while this latter is able of obtaining better solutions than MOCell in large instances. Furthermore, we have observed that the optimal solutions found are composed of a high percentage of low-cost requirements and, also, the requirements that produce most satisfaction on the customers.

Requirements engineering for multiple customers, each of whom have competing and often conflicting priorities, raises issues of negotiation, mediation and conflict resolution. This paper uses a multi-objective optimisation approach to support investigation of the trade-offs in various notions of fairness between multiple customers. Results are presented to validate the approach using two real-world data sets and also using data sets created specifically to stress test the approach. Simple graphical techniques are used to visualize the solution space.

In this position paper, we argue that search based software engineering techniques can be applied to the optimisation problem during the requirements analysis phase. Search based techniques offer significant advantages; they can be used to seek robust, scalable solutions, to perform sensitivity analysis, to yield insight and provide feedback explaining choices to the decision maker. This position paper overviews existing achievements and sets out future challenges.

This paper is concerned with the Multi-Objective Next Release Problem (MONRP), a problem in search-based requirements engineering. Previous work has considered only single objective formulations. In the multi-objective formulation, there are at least two (possibly conflicting) objectives that the software engineer wishes to optimize. It is argued that the multi-objective formulation is more realistic, since requirements engineering is characterised by the presence of many complex and conflicting demands, for which the software engineer must find a suitable balance. The paper presents the results of an empirical study into the suitability of weighted and Pareto optimal genetic algorithms, together with the NSGA-II algorithm, presenting evidence to support the claim that NSGA-II is well suited to the MONRP. The paper also provides benchmark data to indicate the size above which the MONRP becomes non-trivial.