Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186

Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints

User-centric Adaptation Analysis of Multi-tenant Services

Multi-tenancy is a key pillar of cloud services. It allows different users to share computing and virtual resources transparently, meanwhile guaranteeing substantial cost savings. Due to the tradeoff between scalability and customization, one of the major drawbacks of multi-tenancy is limited configurability. Since users may often have conflicting configuration preferences, offering the best user experience is an open challenge for service providers. In addition, the users, their preferences, and the operational environment may change during the service operation, thus jeopardizing the satisfaction of user preferences. In this article, we present an approach to support user-centric adaptation of multi-tenant services. We describe how to engineer the activities of the Monitoring, Analysis, Planning, Execution (MAPE) loop to support user-centric adaptation, and we focus on adaptation analysis. Our analysis computes a service configuration that optimizes user satisfaction, complies with infrastructural constraints, and minimizes reconfiguration obtrusiveness when user- or service-related changes take place. To support our analysis, we model multitenant services and user preferences by using feature and preference models, respectively. We illustrate our approach by utilizing different cases of virtual desktops. Our results demonstrate the effectiveness of the analysis in improving user preferences satisfaction in negligible time.Ministerio de Economía y Competitividad TIN2012-32273Junta de Andalucía P12--TIC--1867Junta de Andalucía TIC-590

Multi-objective test case prioritization in highly configurable systems: A case study

Test case prioritization schedules test cases for execution in an order that attempts to accelerate the detection of faults. The order of test cases is determined by prioritization objectives such as covering code or critical components as rapidly as possible. The importance of this technique has been recognized in the context of Highly-Configurable Systems (HCSs), where the potentially huge number of configurations makes testing extremely challenging. However, current approaches for test case prioritization in HCSs suffer from two main limitations. First, the prioritization is usually driven by a single objective which neglects the potential benefits of combining multiple criteria to guide the detection of faults. Second, instead of using industry-strength case studies, evaluations are conducted using synthetic data, which provides no information about the effectiveness of different prioritization objectives. In this paper, we address both limitations by studying 63 combinations of up to three prioritization objectives in accelerating the detection of faults in the Drupal framework. Results show that non–functional properties such as the number of changes in the features are more effective than functional metrics extracted from the configuration model. Results also suggest that multi-objective prioritization typically results in faster fault detection than mono-objective prioritization.CICYT TIN2012-32273CICYT TIN2015-70560-RJunta de Andalucía P12-TIC- 186

A systematic literature review on the semi-automatic configuration of extended product lines

Product line engineering has become essential in mass customisation given its ability to reduce production costs and time to market, and to improve product quality and customer satisfaction. In product line literature, mass customisation is known as product configuration. Currently, there are multiple heterogeneous contributions in the product line configuration domain. However, a secondary study that shows an overview of the progress, trends, and gaps faced by researchers in this domain is still missing. In this context, we provide a comprehensive systematic literature review to discover which approaches exist to support the configuration process of extended product lines and how these approaches perform in practice. Extend product lines consider non-functional properties in the product line modelling. We compare and classify a total of 66 primary studies from 2000 to 2016. Mainly, we give an in-depth view of techniques used by each work, how these techniques are evaluated and their main shortcomings. As main results, our review identified (i) the need to improve the quality of the evaluation of existing approaches, (ii) a lack of hybrid solutions to support multiple configuration constraints, and (iii) a need to improve scalability and performance conditions

Simulation-based testing of highly configurable cyber-physical systems: automation, optimization and debugging

Sistema Ziber-Fisikoek sistema ziber digitalak sistema fisikoekin uztartzen dituzte. Sistema hauen aldakortasuna handitzen ari da erabiltzaileen hainbat behar betetzeko. Ondorioz, sistema ziber-fisikoa aldakorrak edota produktu lerroak ari dira garatzen eta sistema hauek milaka edo milioika konfiguraziotan konfiguratu daitezke. Sistema ziber-fisiko aldakorren test eta balidazioa prozesua garestia da, batez ere probatu beharreko konfigurazio kopuruaren ondorioz. Konfigurazio kopuru altuak sistemaren prototipo bat erabiltzea ezinezkoa egiten du. Horregatik, sistema ziber-fisiko aldagarriak simulazio modeloak erabilita probatzen dira. Hala ere, simulazio bidez sistema ziber-fisikoak probatzea erronka izaten jarraitzen du. Hasteko, simulazio denbora altua izaten da normalki, software-az aparte, sistema fisikoa simulatu behar delako. Sistema fisiko hau normalean modelo matematiko konplexuen bitartez modelatzen da, konputazionalki garestia delarik. Jarraitzeko, sistema ziber-fisikoek ingeniaritzaren domeinu ezberdinak dituzte tartean, adibidez mekanika edo elektronika. Domeinu bakoitzak bere simulazio erremienta erabiltzen du, eta erremienta guzti hauek interkonektatzeko ko-simulazioa erabiltzen da. Nahiz eta ko-simulazioa abantaila bat izan ematen duen flexibilitateagatik, simulagailu ezberdinen erabilerak simulazio denbora handiagotzen du. Azkenik, sistema ziber-fisikoak simulaziopean probatzean, probak maila ezberdinetan egin behar dira (adb., Model, Software eta Hardware-in-the-Loop mailak), eta honek, proba-kasuak exekutatzeko denbora handitzen du. Tesi honen helburua sistema ziber-fisiko aldakorren test jardunbideak hobetzea da, horretarako automatizazio, optimizazio eta arazketa metodoak proposatzen ditu. Automatizazioari dagokionez, lehenengo, erremienta-bidezko metodologia bat proposatzen da. Metodologia hau test sistema instantziak automatikoki sortzeko gai da, test sistema hauek sistema ziber-fisiko aldagarrien konfigurazioak automatikoki probatzeko gai dira (adb., test orakuluen bitartez). Bigarren, test frogak automatikoki sortzeko planteamendu bat proposatzen da helburu anitzeko bilaketa algoritmoak erabilita. Optimizazioari dagokionez, test frogen aukeraketarako planteamendu bat eta test frogen priorizaziorako beste planteamendu bat proposatzen dira, biak bilaketa alix goritmoak erabiliz, sistema ziber-fisiko aldakorrak test maila ezberdinetan probatzeko helburuarekin. Arazketari dagokionez, “espektroan oinarritutako falten lokalizazioa” izeneko teknika bat produktu lerroen testuingurura adaptatu da, eta faltak isolatzeko metodo bat proposatzen da. Honek, falta ezberdinak lokalizatzea errezten du ez bakarrik sistema ziber-fisiko aldakorretan, baizik eta edozein produktu lerrotan non “feature model” delako modeloak erabiltzen diren aldakortasuna kudeatzeko.Los sistemas cyber-físicos (CPSs) integran tecnologías digitales con procesos físicos. La variabilidad de estos sistemas está creciendo para responder a la demanda de diferentes clientes. Como consecuencia de ello, los CPSs están volviéndose configurables e incluso líneas de producto, lo que significa que pueden ser configurados en miles y millones de configuraciones. El testeo de sistemas cyber-físicos configurables es un proceso costoso, en general debido a la cantidad de configuraciones que han de ser testeadas. El número de configuraciones a testear hace imposible el uso de un prototipo del sistema. Por ello, los sistemas CPSs configurables están siendo testeadas utilizando modelos de simulación. Sin embargo, el testeo de sistemas cyber-físicos bajo simulación sigue siendo un reto. Primero, el tiempo de simulación es normalmente largo, ya que, además del software, la capa física del CPS ha de ser testeada. Esta capa física es típicamente modelada con modelos matemáticos complejos, lo cual es computacionalmente caro. Segundo, los sistemas cyber-físicos implican el uso de diferentes dominios de la ingeniería, como por ejemplo la mecánica o la electrónica. Por ello, para interconectar diferentes herramientas de modelado y simulación hace falta el uso de la co-simulación. A pesar de que la co-simulación es una ventaja en términos de flexibilidad para los ingenieros, el uso de diferentes simuladores hace que el tiempo de simulación sea más largo. Por último, al testear sistemas cyberfísicos haciendo uso de simulación, existen diferentes niveles (p.ej., Model, Software y Hardware-in-the-Loop), lo cual incrementa el tiempo para ejecutar casos de test. Esta tesis tiene como objetivo avanzar en la práctica actual del testeo de sistemas cyber-físicos configurables, proponiendo métodos para la automatización, optimización y depuración. En cuanto a la automatización, primero, se propone una metodología soportada por una herramienta para generar automáticamente instancias de sistemas de test que permiten testear automáticamente configuraciones del sistema CPS configurable (p.ej., haciendo uso de oráculos de test). Segundo, se propone un enfoque para generación de casos de test basado en algoritmos de búsqueda multiobjetivo, los cuales generan un conjunto de casos de test. En cuanto a la optimización, se propone un enfoque para selección y otro para priorización de casos de test, ambos basados en algoritmos de búsqueda, de cara a testear eficientemente sistemas cyberfísicos configurables en diferentes niveles de test. En cuanto a la depuración, se adapta una técnica llamada “Localización de Fallos Basada en Espectro” al contexto de líneas de productos y proponemos un método de aislamiento de fallos. Esto permite localizar bugs no solo en sistemas cyber-físicos configurables sino también en cualquier línea de producto donde se utilicen modelos de características para gestionar la variabilidad.Cyber-Physical Systems (CPSs) integrate digital cyber technologies with physical processes. The variability of these systems is increasing in order to give solution to the different customers demands. As a result, CPSs are becoming configurable or even product lines, which means that they can be set into thousands or millions of configurations. Testing configurable CPSs is a time consuming process, mainly due to the large amount of configurations that need to be tested. The large amount of configurations that need to be tested makes it infeasible to use a prototype of the system. As a result, configurable CPSs are being tested using simulation. However, testing CPSs under simulation is still challenging. First, the simulation time is usually long, since apart of the software, the physical layer needs to be simulated. This physical layer is typically modeled with complex mathematical models, which is computationally very costly. Second, CPSs involve different domains, such as, mechanical and electrical. Engineers of different domains typically employ different tools for modeling their subsystems. As a result, co-simulation is being employed to interconnect different modeling and simulation tools. Despite co-simulation being an advantage in terms of engineers flexibility, the use of different simulation tools makes the simulation time longer. Lastly, when testing CPSs employing simulation, different test levels exist (i.e., Model, Software and Hardware-in-the-Loop), what increases the time for executing test cases. This thesis aims at advancing the current practice on testing configurable CPSs by proposing methods for automation, optimization and debugging. Regarding automation, first, we propose a tool supported methodology to automatically generate test system instances that permit automatically testing configurations of the configurable CPS (e.g., by employing test oracles). Second, we propose a test case generation approach based on multi-objective search algorithms that generate cost-effective test suites. As for optimization, we propose a test case selection and a test case prioritization approach, both of them based on search algorithms, to cost-effectively test configurable CPSs at different test levels. Regarding debugging, we adapt a technique named Spectrum-Based Fault Localization to the product line engineering context and propose a fault isolation method. This permits localizing bugs not only in configurable CPSs but also in any product line where feature models are employed to model variability

How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue -- how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto non-dominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multi-objective optimization could still be relatively new to SE/SBSE researchers, who may not be able to identify the right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multi-objective optimization problems may not be appropriate for specific SE problems, especially when the problem nature or decision maker's preferences are explicitly/implicitly available. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading use of evaluation methods. In this paper, we first carry out a systematic and critical review of quality evaluation for multi-objective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in seven repositories, and select 95 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.Comment: This paper has been accepted by IEEE Transactions on Software Engineering, available as full OA: https://ieeexplore.ieee.org/document/925218

Comparative analysis of classical multi-objective evolutionary algorithms and seeding strategies for pairwise testing of Software Product Lines

Lopez-Herrejon, R. Erick, Ferrer J., Chicano F., Egyed A., & Alba E. (2014). Comparative analysis of classical multi-objective evolutionary algorithms and seeding strategies for pairwise testing of Software Product Lines. Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, July 6-11, 2014. 387–396.Software Product Lines (SPLs) are families of related software products, each with its own set of feature combinations. Their commonly large number of products poses a unique set of challenges for software testing as it might not be technologically or economically feasible to test of all them individually. SPL pairwise testing aims at selecting a set of products to test such that all possible combinations of two features are covered by at least one selected product. Most approaches for SPL pairwise testing have focused on achieving full coverage of all pairwise feature combinations with the minimum number of products to test. Though useful in many contexts, this single-objective perspective does not reflect the prevailing scenario where software engineers do face trade-offs between the objectives of maximizing the coverage or minimizing the number of products to test. In contrast and to address this need, our work is the first to propose a classical multi-objective formalisation where both objectives are equally important. In this paper, we study the application to SPL pairwise testing of four classical multi-objective evolutionary algorithms. We developed three seeding strategies — techniques that leverage problem domain knowledge — and measured their performance impact on a large and diverse corpus of case studies using two well-known multi-objective quality measures. Our study identifies the performance differences among the algorithms and corroborates that the more domain knowledge leveraged the better the search results. Our findings enable software engineers to select not just one solution (as in the case of single-objective techniques) but instead to select from an array of test suite possibilities the one that best matches the economical and technological constraints of their testing context.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Austrian Science Fund (FWF) project P25289- N15 and Lise Meitner Fellowship M1421-N15. Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2011-28194 and fellowship BES-2012-055967. Project 8.06/5.47.4142 in collaboration with the VSB-Tech. Univ. of Ostrava and Universidad de Málaga, Andalucía Tech

An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences

Many-objective optimization is an area of interest common to researchers, professionals, and practitioners because of its real-world implications. Preference incorporation into Multi-Objective Evolutionary Algorithms (MOEAs) is one of the current approaches to treat Many-Objective Optimization Problems (MaOPs). Some recent studies have focused on the advantages of embedding preference models based on interval outranking into MOEAs; several models have been proposed to achieve it. Since there are many factors influencing the choice of the best outranking model, there is no clear notion of which is the best model to incorporate the preferences of the decision maker into a particular problem. This paper proposes a hyper-heuristic algorithm—named HyperACO—that searches for the best combination of several interval outranking models embedded into MOEAs to solve MaOPs. HyperACO is able not only to select the most appropriate model but also to combine the already existing models to solve a specific MaOP correctly. The results obtained on the DTLZ and WFG test suites corroborate that HyperACO can hybridize MOEAs with a combined preference model that is suitable to the problem being solved. Performance comparisons with other state-of-the-art MOEAs and tests for statistical significance validate this conclusion