FEED-FORWARD IN SOFTWARE ENGINEERING WITH PARTICULAR FOCUS ON REQUIREMENTS ENGINEERING AND SOFTWARE ARCHITECTING

This study is intended to determine the characteristics, impact and state of the practice of feed-forward in software engineering; in particular, in the fields of Requirements Engineering (RE) and Software Architecting (SA). Feed-forward is used in many domains such as systems engineering, neural networks, management and psychotherapy. However, in software engineering, especially in RE and SA, the concept of feed-forward is not well researched. For example, what are the characteristics of feed-forward information? What effect does feed-forward information have on architectural artefacts and software project aspects such as cost, quality, time, etc.? What is the current state of practice of feed-forward? A knowledge seeking empirical investigation including an industrial survey and an embedded case study with four projects as four units of analysis were carried out based on these questions. The overall findings ofthis study show that the most common types of information that are fed-forward consistently are requirements and architectural information. This information affects a multitude of aspects of a software project (such as time, cost and quality) and influences several architectural artefacts (such as tactics, patterns and decisions). The results also show that approximately 20% of software professionals have never, or rarely, practiced feed­ forward in their organizations. On the other hand, approximately 66% of software professionals practice feed-forward in their organization in varying levels (“sometimes”, “most of the time”, “always”). 64% of software professionals find feed-forward to be useful for their organization and 4% thought that feed-forward would not be useful, citing reasons such as information overload and lack of motivation. From a researcher’s perspective, determining the properties of feed-forward could provide ground work for doing further research on feed-forward such as: the practice of feed-forward in the other areas of software engineering and the comparison of feedback and feed-forward in software engineerin

A fine-grained requirement traceability evolutionary algorithm: Kromaia, a commercial video game case study

[EN] Context:Commercial video games usually feature an extensive source code and requirements that are related to code lines from multiple methods. Traceability is vital in terms of maintenance and content update, so it is necessary to explore such search spaces properly. Objective:This work presents and evaluates CODFREL (Code Fragment-based Requirement Location), our approach to fine-grained requirement traceability, which lies in an evolutionary algorithm and includes encoding and genetic operators to manipulate code fragments that are built from source code lines. We compare it with a baseline approach (Regular-LSI) by configuring both approaches with different granularities (code lines / complete methods). Method:We evaluated our approach and Regular-LSI in the Kromaia video game case study, which is a commercial video game released on PC and PlayStation 4. The approaches are configured with method and code line granularity and work on 20 requirements that are provided by the development company. Our approach and Regular-LSI calculate similarities between requirements and code fragments or methods to propose possible solutions and, in the case of CODFREL, to guide the evolutionary algorithm. Results:The results, which compare code line and method granularity configurations of CODFREL with different granularity configurations of Regular-LSI, show that our approach outperforms Regular-LSI in precision and recall, with values that are 26 and 8 times better, respectively, even though it does not achieve the optimal solutions. We make an open-source implementation of CODFREL available. Conclusions:Since our approach takes into consideration key issues like the source code size in commercial video games and the requirement dispersion, it provides better starting points than Regular-LSI in the search for solution candidates for the requirements. However, the results and the influence of domain-specific language on them show that more explicit knowledge is required to improve such results.This work has been partially supported by the Ministry of Economy and Competitiveness (MINECO) through the Spanish National R + D + i Plan and ERDF funds under the Project ALPS (RTI2018-096411-B-I00).Blasco, D.; Cetina, C.; Pastor López, O. (2020). A fine-grained requirement traceability evolutionary algorithm: Kromaia, a commercial video game case study. Information and Software Technology. 119:1-12. https://doi.org/10.1016/j.infsof.2019.106235S112119Watkins, R., & Neal, M. (1994). Why and how of requirements tracing. IEEE Software, 11(4), 104-106. doi:10.1109/52.300100Rempel, P., & Mader, P. (2017). Preventing Defects: The Impact of Requirements Traceability Completeness on Software Quality. IEEE Transactions on Software Engineering, 43(8), 777-797. doi:10.1109/tse.2016.2622264Borg, M., Runeson, P., & Ardö, A. (2013). Recovering from a decade: a systematic mapping of information retrieval approaches to software traceability. Empirical Software Engineering, 19(6), 1565-1616. doi:10.1007/s10664-013-9255-yLandauer, T. K., Foltz, P. W., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259-284. doi:10.1080/01638539809545028Poshyvanyk, D., Gueheneuc, Y.-G., Marcus, A., Antoniol, G., & Rajlich, V. (2007). Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval. IEEE Transactions on Software Engineering, 33(6), 420-432. doi:10.1109/tse.2007.1016Dit, B., Revelle, M., Gethers, M., & Poshyvanyk, D. (2011). Feature location in source code: a taxonomy and survey. Journal of Software: Evolution and Process, 25(1), 53-95. doi:10.1002/smr.567Arcuri, A., & Fraser, G. (2013). Parameter tuning or default values? An empirical investigation in search-based software engineering. Empirical Software Engineering, 18(3), 594-623. doi:10.1007/s10664-013-9249-9Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77-89. doi:10.1016/s0034-4257(97)00083-7Apache opennlp: Toolkit for the processing of natural language text, 2017, (https://opennlp.apache.org/). [Online; accessed 12-November-2017].P. Abeles, Efficient java matrix library, 2017, (http://ejml.org/). [Online; accessed 9-November-2017].IGDA, International Game Developers Association, 2018.Lucia, A. D., Fasano, F., Oliveto, R., & Tortora, G. (2007). Recovering traceability links in software artifact management systems using information retrieval methods. ACM Transactions on Software Engineering and Methodology, 16(4), 13. doi:10.1145/1276933.1276934De Lucia, A., Oliveto, R., & Tortora, G. (2008). Assessing IR-based traceability recovery tools through controlled experiments. Empirical Software Engineering, 14(1), 57-92. doi:10.1007/s10664-008-9090-8Zou, X., Settimi, R., & Cleland-Huang, J. (2009). Improving automated requirements trace retrieval: a study of term-based enhancement methods. Empirical Software Engineering, 15(2), 119-146. doi:10.1007/s10664-009-9114-zUnterkalmsteiner, M., Gorschek, T., Feldt, R., & Lavesson, N. (2015). Large-scale information retrieval in software engineering - an experience report from industrial application. Empirical Software Engineering, 21(6), 2324-2365. doi:10.1007/s10664-015-9410-8Bavota, G., De Lucia, A., Oliveto, R., & Tortora, G. (2014). Enhancing software artefact traceability recovery processes with link count information. Information and Software Technology, 56(2), 163-182. doi:10.1016/j.infsof.2013.08.00

Considerations about quality in model-driven engineering

The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.F.G, would like to thank COLCIENCIAS (Colombia) for funding this work through the Colciencias Grant call 512-2010. This work has been supported by the Gene-ralitat Valenciana Project IDEO (PROMETEOII/2014/039), the European Commission FP7 Project CaaS (611351), and ERDF structural funds.Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. https://doi.org/10.1007/s11219-016-9350-6S166(1985). Iso information processing—documentation symbols and conventions for data, program and system flowcharts, program network charts and system resources charts. ISO 5807:1985(E) (pp. 1–25).(2011). Iso/iec/ieee systems and software engineering – architecture description. 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Quality in model-driven engineering: a tertiary study. Software Quality Journal, 1–33.Grobshtein, Y., & Dori, D. (2011). Generating sysml views from an opm model: design and evaluation. Systems Engineering, 14(3), 327–340.Haan, J.d. (2008). 8 reasons why model-driven approaches (will) fail.Harel, D., & Rumpe, B. (2000). Modeling languages: Syntax, semantics and all that stuff, part i: The basic stuff, Israel. Technical report Jerusalem Israel.Harel, D., & Rumpe, B. (2004). Meaningful modeling: what’s the semantics of semantics? Computer, 37(10), 64–72.Hebig, R., & Bendraou, R. (2014). On the need to study the impact of model driven engineering on software processes. In Proceedings of the 2014 International Conference on Software and System Process, ICSSP 2014 (pp. 164–168). New York: ACM.Heidari, F., & Loucopoulos, P. (2014). Quality evaluation framework (qef): modeling and evaluating quality of business processes. 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In Nierstrasz, O., Whittle, J., Harel, D., & Reggio, G. (Eds.) Model Driven Engineering Languages and Systems, volume 4199 of Lecture Notes in Computer Science (pp. 27–41). Berlin, Heidelberg: Springer.Lange, C.F.J., & Chaudron, M.R.V. (2006). Effe

On Evidence-based Risk Management in Requirements Engineering

Background: The sensitivity of Requirements Engineering (RE) to the context makes it difficult to efficiently control problems therein, thus, hampering an effective risk management devoted to allow for early corrective or even preventive measures. Problem: There is still little empirical knowledge about context-specific RE phenomena which would be necessary for an effective context- sensitive risk management in RE. Goal: We propose and validate an evidence-based approach to assess risks in RE using cross-company data about problems, causes and effects. Research Method: We use survey data from 228 companies and build a probabilistic network that supports the forecast of context-specific RE phenomena. We implement this approach using spreadsheets to support a light-weight risk assessment. Results: Our results from an initial validation in 6 companies strengthen our confidence that the approach increases the awareness for individual risk factors in RE, and the feedback further allows for disseminating our approach into practice.Comment: 20 pages, submitted to 10th Software Quality Days conference, 201

On Integrating Student Empirical Software Engineering Studies with Research and Teaching Goals

Background: Many empirical software engineering studies use students as subjects and are conducted as part of university courses. Aim: We aim at reporting our experiences with using guidelines for integrating empirical studies with our research and teaching goals. Method: We document our experience from conducting three studies with graduate students in two software architecture courses. Results: Our results show some problems that we faced when following the guidelines and deviations we made from the original guidelines. Conclusions: Based on our results we propose recommendations for empirical software engineering studies that are integrated in university courses.

Quality-aware model-driven service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Quality aspects ranging from interoperability to maintainability to performance are of central importance for the integration of heterogeneous, distributed service-based systems. Architecture models can substantially influence quality attributes of the implemented software systems. Besides the benefits of explicit architectures on maintainability and reuse, architectural constraints such as styles, reference architectures and architectural patterns can influence observable software properties such as performance. Empirical performance evaluation is a process of measuring and evaluating the performance of implemented software. We present an approach for addressing the quality of services and service-based systems at the model-level in the context of model-driven service engineering. The focus on architecture-level models is a consequence of the black-box character of services