On Improving Automation by Integrating RFID in the Traceability Management of the Agri-Food Sector

Traceability is a key factor for the agri-food sector. RFID technology, widely adopted for supply chain management, can be used effectively for the traceability management. In this paper, a framework for the evaluation of a traceability system for the agri-food industry is presented and the automation level in an RFID-based traceability system is analyzed and compared with respect to traditional ones. Internal and external traceability are both considered and formalized, in order to classify different environments, according to their automation level. Traceability systems used in a sample sector are experimentally analyzed, showing that by using RFID technology, agri-food enterprises increase their automation level and also their efficiency, in a sustainable wa

Collaborative traceability management: a multiple case study from the perspectives of organization, process, and culture

Traceability is crucial for many activities in software and systems engineering including monitoring the development progress, and proving compliance with standards. In practice, the use and maintenance of trace links are challenging as artifacts undergo constant change, and development takes place in distributed scenarios with multiple collaborating stakeholders. Although traceability management in general has been addressed in previous studies, there is a need for empirical insights into the collaborative aspects of traceability management and how it is situated in existing development contexts. The study reported in this paper aims to close this gap by investigating the relation of collaboration and traceability management, based on an understanding of characteristics of the development effort. In our multiple exploratory case study, we conducted semi-structured interviews with 24 individuals from 15 industrial projects. We explored which challenges arise, how traceability management can support collaboration, how collaboration relates to traceability management approaches, and what characteristics of the development effort influence traceability management and collaboration. We found that practitioners struggle with the following challenges: (1) collaboration across team and tool boundaries, (2) conveying the benefits of traceability, and (3) traceability maintenance. If these challenges are addressed, we found that traceability can facilitate communication and knowledge management in distributed contexts. Moreover, there exist multiple approaches to traceability management with diverse collaboration approaches, i.e., requirements-centered, developer-driven, and mixed approaches. While traceability can be leveraged in software development with both agile and plan-driven paradigms, a certain level of rigor is needed to realize its benefits and overcome challenges. To support practitioners, we provide principles of collaborative traceability management. The main contribution of this paper is empirical evidence of how culture, processes, and organization impact traceability management and collaboration, and principles to support practitioners with collaborative traceability management. We show that collaboration and traceability management have the potential to be mutually beneficial—when investing in one, also the other one is positively affected

Collaborative traceability management: a multiple case study from the perspectives of organization, process, and culture

Traceability is crucial for many activities in software and systems engineering including monitoring the development progress, and proving compliance with standards. In practice, the use and maintenance of trace links are challenging as artifacts undergo constant change, and development takes place in distributed scenarios with multiple collaborating stakeholders. Although traceability management in general has been addressed in previous studies, there is a need for empirical insights into the collaborative aspects of traceability management and how it is situated in existing development contexts. The study reported in this paper aims to close this gap by investigating the relation of collaboration and traceability management, based on an understanding of characteristics of the development effort. In our multiple exploratory case study, we conducted semi-structured interviews with 24 individuals from 15 industrial projects. We explored which challenges arise, how traceability management can support collaboration, how collaboration relates to traceability management approaches, and what characteristics of the development effort influence traceability management and collaboration. We found that practitioners struggle with the following challenges: (1) collaboration across team and tool boundaries, (2) conveying the benefits of traceability, and (3) traceability maintenance. If these challenges are addressed, we found that traceability can facilitate communication and knowledge management in distributed contexts. Moreover, there exist multiple approaches to traceability management with diverse collaboration approaches, i.e., requirements-centered, developer-driven, and mixed approaches. While traceability can be leveraged in software development with both agile and plan-driven paradigms, a certain level of rigor is needed to realize its benefits and overcome challenges. To support practitioners, we provide principles of collaborative traceability management. The main contribution of this paper is empirical evidence of how culture, processes, and organization impact traceability management and collaboration, and principles to support practitioners with collaborative traceability management. We show that collaboration and traceability management have the potential to be mutually beneficial—when investing in one, also the other one is positively affected

Assessing Traceability of Software Engineering Artifacts

The generation of traceability links or traceability matrices is vital to many software engineering activities. It is also person-power intensive, time-consuming, error-prone, and lacks tool support. The activities that require traceability information include, but are not limited to, risk analysis, impact analysis, criticality assessment, test coverage analysis, and verification and validation of software systems. Information Retrieval (IR) techniques have been shown to assist with the automated generation of traceability links by reducing the time it takes to generate the traceability mapping. Researchers have applied techniques such as Latent Semantic Indexing (LSI), vector space retrieval, and probabilistic IR and have enjoyed some success. This paper concentrates on examining issues not previously widely studied in the context of traceability: the importance of the vocabulary base used for tracing and the evaluation and assessment of traceability mappings and methods using secondary measures. We examine these areas and perform empirical studies to understand the importance of each to the traceability of software engineering artifacts

Exploring the potentials and tools of systems engineering and MBSE in machine design

Abstract. This thesis explores Systems Engineering (SE) and Model-Based Systems Engineering (MBSE) in the context of modern machine design. The primary objective is to understand how SE’s interdisciplinary and holistic methodologies, once rooted in the telephone industry, can be seamlessly adapted into the intricate realm of machine design. One of the key findings suggests that, despite the growing intrigue around MBSE as a novel approach to systems engineering, there is still a lack of concrete evidence to substantiate its effectiveness. However, certain studies have highlighted the strengths of MBSE, especially its tools’ capability for parametric and numerical analyses. These tools integrate smoothly with the initial phases of the design process, enabling continuous exploration of a system’s dynamic behavior. While MBSE is still emerging, it offers several apparent advantages, such as improved communication, increased consistency, and efficient use of both time and financial resources. With the knowledge that mechanical engineering these days means working with many different specialists from various fields, we can safely say that engineering machines like cars and planes fall into the realm of systems engineering. The primary methodology employed for data acquisition in this thesis was a literature review.Systeemitekniikan ja MBSE:n mahdollisuudet ja työkalut koneensuunnittelussa. Tiivistelmä. Tämä opinnäytetyö tutkii Systeemitekniikan (SE) ja Mallipohjaisen Systeemitekniikan (MBSE) käsitteitä modernin koneensuunnittelun kontekstissa. Pääasiallinen tavoite on ymmärtää, miten SE:n monitieteelliset ja kokonaisvaltaiset menetelmät, jotka alun perin juontavat juurensa puhelinalaan, voivat saumattomasti soveltua monimutkaisen koneensuunnittelun maailmaan. Yksi keskeisistä havainnoista viittaa siihen, että vaikka MBSE herättää kasvavaa kiinnostusta uutena lähestymistapana systeemitekniikkaan, sen tehokkuutta tukevasta konkreettisesta näytöstä on edelleen niukasti saatavilla. Kuitenkin tietyt tutkimukset ovat korostaneet MBSE:n vahvuuksia, erityisesti sen työkalujen kykyä parametriseen ja numeeriseen analyysiin. Nämä työkalut integroituvat saumattomasti suunnitteluprosessin alkuvaiheisiin, mahdollistaen järjestelmän dynaamisen käyttäytymisen jatkuvan tutkimisen. Vaikka MBSE on edelleen kehittyvä alue, se tarjoaa useita selkeitä etuja, kuten parannetun kommunikaation, lisääntyneen johdonmukaisuuden sekä ajan ja taloudellisten resurssien tehokkaamman hyödyntämisen. Kun otetaan huomioon, että nykyaikainen koneensuunnittelu edellyttää usein yhteistyötä eri alojen erikoisasiantuntijoiden kanssa, voidaan perustellusti väittää, että monimutkaisten koneiden, kuten autojen ja lentokoneiden, suunnittelu kuuluu systeemitekniikan piiriin. Tämän opinnäytetyön tärkein tutkimusmenetelmä oli kirjallisuuskatsaus

Can Clustering Improve Requirements Traceability? A Tracelab-enabled Study

Software permeates every aspect of our modern lives. In many applications, such in the software for airplane flight controls, or nuclear power control systems software failures can have catastrophic consequences. As we place so much trust in software, how can we know if it is trustworthy? Through software assurance, we can attempt to quantify just that. Building complex, high assurance software is no simple task. The difficult information landscape of a software engineering project can make verification and validation, the process by which the assurance of a software is assessed, very difficult. In order to manage the inevitable information overload of complex software projects, we need software traceability, the ability to describe and follow the life of a requirement, in both forwards and backwards direction. The Center of Excellence for Software Traceability (CoEST) has created a compelling research agenda with the goal of ubiquitous traceability by 2035. As part of this goal, they have developed TraceLab, a visual experimental workbench built to support design, implementation, and execution of traceability experiments. Through our collaboration with CoEST, we have made several contributions to TraceLab and its community. This work contributes to the goals of the traceability research community. The three key contributions are (a) a machine learning component package for TraceLab featuring six (6) classifier algorithms, five (5) clustering algorithms, and a total of over 40 components for creating TraceLab experiments, built upon the WEKA machine learning package, as well as implementing methods outside of WEKA; (b) the design for an automated tracing system that uses clustering to decompose the task of tracing into many smaller tracing subproblems; and (c) an implementation of several key components of this tracing system using TraceLab and its experimental evaluation

Contingency Management Requirements Document: Preliminary Version. Revision F

This is the High Altitude, Long Endurance (HALE) Remotely Operated Aircraft (ROA) Contingency Management (CM) Functional Requirements document. This document applies to HALE ROA operating within the National Airspace System (NAS) limited at this time to enroute operations above 43,000 feet (defined as Step 1 of the Access 5 project, sponsored by the National Aeronautics and Space Administration). A contingency is an unforeseen event requiring a response. The unforeseen event may be an emergency, an incident, a deviation, or an observation. Contingency Management (CM) is the process of evaluating the event, deciding on the proper course of action (a plan), and successfully executing the plan

Locating bugs without looking back

Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We compare our approach to eight others, using their own five metrics on their own six open source projects. Out of 30 performance indicators, we improve 27 and equal 2. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. These results show the applicability of our approach to software projects without history