Traceability Links Recovery among Requirements and BPMN models

Tesis por compendio[EN] Throughout the pages of this document, I present the results of the research that was carried out in the context of my PhD studies. During the aforementioned research, I studied the process of Traceability Links Recovery between natural language requirements and industrial software models. More precisely, due to their popularity and extensive usage, I studied the process of Traceability Links Recovery between natural language requirements and Business Process Models, also known as BPMN models. In order to carry out the research, I focused my work on two main objectives: (1) the development of the Traceability Links Recovery techniques between natural language requirements and BPMN models, and (2) the validation and analysis of the results obtained by the developed techniques in industrial domain case studies. The results of the research have been redacted and published in forums, conferences, and journals specialized in the topics and context of the research. This thesis document introduces the topics, context, and objectives of the research, presents the academic publications that have been published as a result of the work, and then discusses the outcomes of the investigation.[ES] A través de las páginas de este documento, presento los resultados de la investigación realizada en el contexto de mis estudios de doctorado. Durante la investigación, he estudiado el proceso de Recuperación de Enlaces de Trazabilidad entre requisitos especificados en lenguaje natural y modelos de software industriales. Más concretamente, debido a su popularidad y uso extensivo, he estudiado el proceso de Recuperación de Enlaces de Trazabilidad entre requisitos especificados en lenguaje natural y Modelos de Procesos de Negocio, también conocidos como modelos BPMN. Para llevar a cabo esta investigación, mi trabajo se ha centrado en dos objetivos principales: (1) desarrollo de técnicas de Recuperación de Enlaces de Trazabilidad entre requisitos especificados en lenguaje natural y modelos BPMN, y (2) validación y análisis de los resultados obtenidos por las técnicas desarrolladas en casos de estudio de dominios industriales. Los resultados de la investigación han sido redactados y publicados en foros, conferencias y revistas especializadas en los temas y contexto de la investigación. Esta tesis introduce los temas, contexto y objetivos de la investigación, presenta las publicaciones académicas que han sido publicadas como resultado del trabajo, y expone los resultados de la investigación.[CA] A través de les pàgines d'aquest document, presente els resultats de la investigació realitzada en el context dels meus estudis de doctorat. Durant la investigació, he estudiat el procés de Recuperació d'Enllaços de Traçabilitat entre requisits especificats en llenguatge natural i models de programari industrials. Més concretament, a causa de la seua popularitat i ús extensiu, he estudiat el procés de Recuperació d'Enllaços de Traçabilitat entre requisits especificats en llenguatge natural i Models de Processos de Negoci, també coneguts com a models BPMN. Per a dur a terme aquesta investigació, el meu treball s'ha centrat en dos objectius principals: (1) desenvolupament de tècniques de Recuperació d'Enllaços de Traçabilitat entre requisits especificats en llenguatge natural i models BPMN, i (2) validació i anàlisi dels resultats obtinguts per les tècniques desenvolupades en casos d'estudi de dominis industrials. Els resultats de la investigació han sigut redactats i publicats en fòrums, conferències i revistes especialitzades en els temes i context de la investigació. Aquesta tesi introdueix els temes, context i objectius de la investigació, presenta les publicacions acadèmiques que han sigut publicades com a resultat del treball, i exposa els resultats de la investigació.Lapeña Martí, R. (2020). Traceability Links Recovery among Requirements and BPMN models [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149391TESISCompendi

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

Scribe: A Clustering Approach To Semantic Information Retrieval

Information retrieval is the process of fulfilling a user?s need for information by locating items in a data collection that are similar to a complex query that is often posed in natural language. Latent Semantic Indexing (LSI) was the predominant technique employed at the National Institute of Standards and Technology?s Text Retrieval Conference for many years until limitations of its scalability to large data sets were discovered. This thesis describes SCRIBE, a modification of LSI with improved scalability. SCRIBE clusters its semantic index into discrete volumes described by high-dimensional extensions to computer graphics data structures. SCRIBE?s clustering strategy limits the number of items that must be searched and provides for sub-linear time complexity in the number of documents. Experimental results with a large, natural language document collection demonstrate that SCRIBE achieves retrieval accuracy similar to LSI but requires 1/10 the time

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Matrix decomposition plays an increasingly significant role in many scientific and engineering applications. Among numerous techniques, Singular Value Decomposition (SVD) and Eigenvalue Decomposition (EVD) are widely used as factorization tools to perform Principal Component Analysis for dimensionality reduction and pattern recognition in image processing, text mining and wireless communications, while QR Decomposition (QRD) and sparse LU Decomposition (LUD) are employed to solve the dense or sparse linear system of equations in bioinformatics, power system and computer vision. Matrix decompositions are computationally expensive and their sequential implementations often fail to meet the requirements of many time-sensitive applications. The emergence of reconfigurable computing has provided a flexible and low-cost opportunity to pursue high-performance parallel designs, and the use of FPGAs has shown promise in accelerating this class of computation. In this research, we have proposed and implemented several highly parallel FPGA-based architectures to accelerate matrix decompositions and their applications in data mining and signal processing. Specifically, in this dissertation we describe the following contributions: • We propose an efficient FPGA-based double-precision floating-point architecture for EVD, which can efficiently analyze large-scale matrices. • We implement a floating-point Hestenes-Jacobi architecture for SVD, which is capable of analyzing arbitrary sized matrices. • We introduce a novel deeply pipelined reconfigurable architecture for QRD, which can be dynamically configured to perform either Householder transformation or Givens rotation in a manner that takes advantage of the strengths of each. • We design a configurable architecture for sparse LUD that supports both symmetric and asymmetric sparse matrices with arbitrary sparsity patterns. • By further extending the proposed hardware solution for SVD, we parallelize a popular text mining tool-Latent Semantic Indexing with an FPGA-based architecture. • We present a configurable architecture to accelerate Homotopy l1-minimization, in which the modification of the proposed FPGA architecture for sparse LUD is used at its core to parallelize both Cholesky decomposition and rank-1 update. Our experimental results using an FPGA-based acceleration system indicate the efficiency of our proposed novel architectures, with application and dimension-dependent speedups over an optimized software implementation that range from 1.5ÃÂ to 43.6ÃÂ in terms of computation time

Configuring and Assembling Information Retrieval based Solutions for Software Engineering Tasks.

Information Retrieval (IR) approaches are used to leverage textual or unstructured data generated during the software development process to support various software engineering (SE) tasks (e.g., concept location, traceability link recovery, change impact analysis, etc.). Two of the most important steps for applying IR techniques to support SE tasks are preprocessing the corpus and configuring the IR technique, and these steps can significantly influence the outcome and the amount of effort developers have to spend for these maintenance tasks. We present the use of Genetic Algorithms (GAs) to automatically configure and assemble an IR process to support SE tasks. The approach named IR-GA determines the (near) optimal solution to be used for each step of the IR process without requiring any training. We applied IR-GA on three different SE tasks and the results of the study indicate that IR-GA outperforms approaches previously used in the literature, and that it does not significantly differ from an ideal upper bound that could be achieved by a supervised approach and a combinatorial approach

Symbolic Universes: a specific declination within the Health Institution

The goal of this paper is to identify and interpret the relationship between the Symbolic Universes (S.U.) and their specific declination in the health context, more precisely how it is expressed in the focus groups on vaccinations. I will introduce to the reader the theoretical background that substantiates and motivates research. Starting from the adoption of the culturalist approach to psychological discipline, I will report the characteristics that distinguish it and the critical issues it tries to face. I will continue by illustrating the results of the first phase of the Re.Cri.Re. through the identification and definition of symbolic universes. I will then illustrate the methods and tools used to carry out research, the results obtained from the analysis and the interpretation of the results

From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting

Computing Network of Diseases and Pharmacological Entities through the Integration of Distributed Literature Mining and Ontology Mapping

The proliferation of -omics (such as, Genomics, Proteomics) and -ology (such as, System Biology, Cell Biology, Pharmacology) have spawned new frontiers of research in drug discovery and personalized medicine. A vast amount (21 million) of published research results are archived in the PubMed and are continually growing in size. To improve the accessibility and utility of such a large number of literatures, it is critical to develop a suit of semantic sensitive technology that is capable of discovering knowledge and can also infer possible new relationships based on statistical co-occurrences of meaningful terms or concepts. In this context, this thesis presents a unified framework to mine a large number of literatures through the integration of latent semantic analysis (LSA) and ontology mapping. In particular, a parameter optimized, robust, scalable, and distributed LSA (DiLSA) technique was designed and implemented on a carefully selected 7.4 million PubMed records related to pharmacology. The DiLSA model was integrated with MeSH to make the model effective and efficient for a specific domain. An optimized multi-gram dictionary was customized by mapping the MeSH to build the DiLSA model. A fully integrated web-based application, called PharmNet, was developed to bridge the gap between biological knowledge and clinical practices. Preliminary analysis using the PharmNet shows an improved performance over global LSA model. A limited expert evaluation was performed to validate the retrieved results and network with biological literatures. A thorough performance evaluation and validation of results is in progress