Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

Grand Challenges of Traceability: The Next Ten Years

In 2007, the software and systems traceability community met at the first Natural Bridge symposium on the Grand Challenges of Traceability to establish and address research goals for achieving effective, trustworthy, and ubiquitous traceability. Ten years later, in 2017, the community came together to evaluate a decade of progress towards achieving these goals. These proceedings document some of that progress. They include a series of short position papers, representing current work in the community organized across four process axes of traceability practice. The sessions covered topics from Trace Strategizing, Trace Link Creation and Evolution, Trace Link Usage, real-world applications of Traceability, and Traceability Datasets and benchmarks. Two breakout groups focused on the importance of creating and sharing traceability datasets within the research community, and discussed challenges related to the adoption of tracing techniques in industrial practice. Members of the research community are engaged in many active, ongoing, and impactful research projects. Our hope is that ten years from now we will be able to look back at a productive decade of research and claim that we have achieved the overarching Grand Challenge of Traceability, which seeks for traceability to be always present, built into the engineering process, and for it to have "effectively disappeared without a trace". We hope that others will see the potential that traceability has for empowering software and systems engineers to develop higher-quality products at increasing levels of complexity and scale, and that they will join the active community of Software and Systems traceability researchers as we move forward into the next decade of research

Analysing Source Code Structure and Mining Software Repositories to Create Requirements Traceability Links

Resume La traçabilité est le seul moyen de s'assurer que le code source d'un système est conforme aux exigences et que toutes ces exigences et uniquement celles-ci ont été implantées par les développeurs. Lors de la maintenance et de l'évolution, les développeurs ajoutent, suppriment ou modifient des fonctionnalités (y compris les fautes) dans le code source. Les liens de traçabilité deviennent alors obsolètes car les développeurs n'ont pas ou ne peuvent pas consacrer les efforts nécessaires pour les mettre à jour. Pourtant, la récupération de liens de traçabilité est une tâche ardue et coûteuse pour les développeurs. Par conséquent, nous trouvons dans la littérature des méthodes, des techniques et des outils pour récupérer ces liens de traçabilité automatiquement ou semi-automatiquement. Parmi les techniques proposées, la littérature montre que les techniques de recherche d'information (RI) peuvent récupérer automatiquement des liens de traçabilité entre les exigences écrites sous forme textuelle et le code source. Toutefois, la précision et le rappel des techniques RI sont affectés par certains facteurs qui influencent les entrées du processus de traçabilité des exigences. En raison de la faible précision et du faible rappel de ces techniques, la confiance des développeurs en l'efficacité des techniques de récupération des liens de traçabilité est affectée négativement. Dans cette thèse, notre proposition est que l'ajout de nouvelles sources d'information et l'intégration des connaissances des développeurs à l'aide d'un modèle de confiance pourrait atténuer l'impact de ces facteurs et améliorer la précision et le rappel des techniques RI. Notre hypothèse est que la précision et le rappel des techniques RI pourraient être améliorés si deux (ou plus) sources d'information confirment un lien de traçabilité. Nous utilisons les données des référentiels logiciels, les relations entre classes, le partitionnement du code source, et les connaissances des développeurs comme sources d'informations supplémentaires pour confirmer un lien. Nous proposons quatre nouvelles approches de détection des liens de traçabilité : Histrace, BCRTrace, Partrace et une dernière basée sur les connaissances des développeurs. Nous proposons un modèle de confiance, Trumo, inspiré des modèles de confiance Web, pour combiner les votes des experts. Nous proposons alors quatre approche de recouvrement des liens de traçabilité : Trustrace, LIBCROOS, COPARVO et une nouvelle méthode d'attribution de poids, qui utilisent les experts créés précédemment et Trumo. Les approches proposées utilisent une technique de recherche d'information pour créer des liens de référence et utilisent l'opinion des experts pour réévaluer ces liens de référence. Nous montrons que l'utilisation de plusieurs sources d'information améliore la précision et le rappel des techniques RI pour la traçabilité des exigences. Les résultats obtenus dans cette thèse montrent une amélioration de jusqu'à 22% de précision, 7% de rappel et 83% de réduction d'effort des développeurs pour la suppression manuelle de liens faux positifs. Les résultats obtenus dans cette thèse sont prometteurs et nous espérons que d'autres recherches dans ce domaine pourraient améliorer notre précision et rappel encore plus.----------ABSTRACT Traceability is the only means to ensure that the source code of a system is consistent with its requirements and that all and only the specified requirements have been implemented. During software maintenance and evolution, as developers add, remove, or modify features (including bugs), requirement traceability links become obsolete because developers do not/cannot devote effort to update them. Yet, recovering these traceability links later is a daunting and costly task for developers. Consequently, the literature proposed methods, techniques, and tools to recover semi-automatically or automatically these traceability links. Among the proposed techniques, the literature showed that information retrieval (IR) techniques can automatically recover traceability links between free-text requirements and source code. However, precision and recall of IR techniques are impacted by some factors, which impact the input of requirements traceability process. Due to low precision and--or recall, developers' confidence in the effectiveness of traceability link recovery technique is negatively affected. In this dissertation, our thesis is that adding more sources of information using a trust-based model and integrating developers' knowledge in automated IR-based requirements traceability approaches could mitigate the impact of the factors and improve the precision and recall of IR techniques. Our conjecture is that the accuracy of information retrieval techniques could be improved if two (or more) sources of information vote for a link. We use software repositories' data, binary class relationships, source code partitioning, and developer's knowledge as extra sources of information to confirm a link. We propose four approaches, Histrace, BCRTrace, Partrace, and developer's knowledge, to create experts out of the available extra sources of information. We propose a trust-model, Trumo, inspired by Web trust-models of users, to combine the experts' votes. We then propose four traceability link recovery approaches: Trustrace, LIBCROOS, COPARVO, and an improved term weighting scheme, which use the experts created by previous four techniques and Trumo. The proposed approaches use an IR technique to create the baseline links and use experts' opinions to reevaluate baseline links. We show that using more sources of information improve the accuracy of IR techniques for requirements traceability. The achieved results in this dissertation show up to 22% precision, 7% recall improvement and 83% reduction in developer's effort for manually removing false-positive links. The results achieved in this dissertation are promising and we hope that further research in this field might improve the accuracy of IR techniques more

Detecting Important Terms in Source Code for Program Comprehension

Software Engineering research has become extremely dependent on terms (words in textual data) extracted from source code. Different techniques have been proposed to extract the most important\u27\u27 terms from code. These terms are typically used as input to research prototypes: the quality of the output of these prototypes will depend on the quality of the term extraction technique. At present no consensus exists about which technique predicts the best terms for code comprehension. We perform a literature review, and propose a unified prediction model based on a Naive Bayes algorithm. We evaluate our model in a field study with professional programmers, as well as a standard 10-fold synthetic study. We found our model predicts the top quartile of the most-important terms with approximately 50% precision and recall, outperforming other popular techniques. We found the predictions from our model to help programmers to the same degree as the gold set

Traceability -- A Literature Review

In light of recent food safety crises and international trade concerns associated with food or animal associated diseases, traceability has once again become important in the minds of public policymakers, business decision makers, consumers and special interest groups. This study reviews studies on traceability, government regulation and consumer behaviour, provide case studies of current traceability systems and a rough breakdown of various costs and benefits of traceability. This report aims to identify gaps that may currently exist in the literature on traceability in the domestic beef supply chain, as well as provide possible directions for future research into said issue. Three main conclusions can be drawn from this study. First, there is a lack of a common definition of traceability. Hence identifying similarities and differences across studies becomes difficult if not impossible. To this end, this study adopts CFIA’s definition of traceability. This definition has been adopted by numerous other agencies including the EU’s official definition of traceability however it may or may not be acceptable from the perspective of major Canadian beef and cattle trade partners. Second, the studies reviewed in this report address one or more of five key objectives; the impact of changing consumer behaviour on market participants, suppliers incentive to adopt or participate in traceability, impact of regulatory changes, supplier response to crisis and technical description of traceability systems. Drawing from the insights from the consumer studies, it seems as if consumers do not value traceability per se, traceability is a means for consumers to receive validation of another production or process attribute that they are interested in. Moreover, supply chain improvement, food safety control and accessing foreign market segments are strong incentives for primary producers and processors to participate in programs with traceability features. However the objectives addressed by the studies reviewed in this paper are not necessarily the objectives that are of most immediate relevance to decision makers about appropriate traceability standards to recommend, require, subsidize etc. In many cases the research objectives of previous work have been extremely narrow creating a body of literature that is incomplete in certain key areas. Third, case studies of existing traceability systems in Australia, the UK, Scotland, Brazil and Uruguay indicate that the pattern of development varies widely across sectors and regions. In summary, a traceability system by itself cannot provide value-added for all participants in the industry; it is merely a protocol for documenting and sharing information. Value is added to participants in the marketing chain through traceability in the form of reduced transactions costs in the case of a food safety incident and through the ability to shift liability. To ensure consumer benefit and have premiums returned to primary producers the type of information that consumers value is an important issue for future research. A successful program that peaks consumer interest and can enhance their eating experience can generate economic benefits to all sectors in the beef industry. International market access will increasingly require traceability in the marketing system in order to satisfy trade restrictions in the case of animal diseases and country of origin labelling, to name only a few examples. Designing appropriate traceability protocols industry wide is therefore becoming very important.traceability, institutions, Canada, consumer behaviour, producer behaviour, supply chain, Agricultural and Food Policy, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Health Economics and Policy, International Relations/Trade, Livestock Production/Industries, Marketing, Production Economics, D020, D100, D200, Q100,

On the Influence of Representation Type and Gender on Recognition Tasks of Program Comprehension

RÉSUMÉ L’objectif de la maintenance logicielle est d’améliorer les logiciels existants en préservant leur intégrité. La maintenance peut représenter jusqu’à 60% du budget d’un logiciel. Ainsi, améliorer la maintenabilité des logiciels est bénéfique aussi bien pour les fournisseurs que les utilisateurs de logiciels. Les développeurs de logiciels consacrent un effort considérable à la compréhension des programmes, qui est une étape primordiale à la maintenance logicielle. Nous faisons l’hypothèque que le genre des développeurs de logiciels et le type de représentation peut affecter leur effort et leur efficacité. Ces facteurs doivent être considérés et minutieusement analysés dans la mesure où ils peuvent cacher certains effets significatifs pouvant être identifiés en analysant le processus de compréhension. Dans cette thèse, nous nous inspirons de l’utilisation de l’occulomètre pour l’étude du processus cognitif lors de la résolution des problèmes. Nous avons effectué une étude fonctionnelle pour évaluer tous les travaux de recherche faisant usage de l’occulomètre en génie logiciel. Les résultats obtenus nous ont motivé à utiliser l’occulomètre pour effectuer un ensemble d’études afin analyser l’effet de deux facteurs importants sur la compréhension des programmes : le type de représentation (textuelle ou graphique) et le genre du développeur. Afin de comprendre comment les différents types de représentations et le genre influencent les stratégies de visualisation, nous avons étudié la différence de stratégie entre développeurs. Les résultats obtenus montrent que, comparé à une représentation graphique, la représentation sous forme de texte structuré aide mieux le développeur dans son processus cognitif lors de la compréhension des programmes de petite taille. Ainsi, la représentation textuelle requiert moins de temps et d’effort aux participants. Par contre, la représentation graphique est celle préférée par les développeurs. Nos résultats montrent que la structure topologique de la représentation graphique aide les développeurs à mémoriser l’emplacement des éléments et à retrouver plus rapidement les éléments pertinents comparé à la représentation textuelle. En plus, la structure hiérarchique de la représentation graphique guide les développeurs à suivre une stratégie de visualisation spécifique. Nous avons observé que les femmes et les hommes ont des stratégies de visualisation différentes lors de la lecture du code ou de la mémorisation des noms des identificateurs. Les femmes ont tendance à inspecter minutieusement toutes les options afin de procéder à l’élimination de la mauvaise réponse. Au contraire, les hommes ont tendance à inspecter brièvement certaines réponses. Pendant que les femmes consacrent plus de temps à analyser chaque type d’entité l’un après l’autre, les hommes alternent leur attention entre différents type d’entité.----------ABSTRACT The purpose of software maintenance is to correct and enhance an existing software system while preserving its integrity. Software maintenance can cost more than 60% of the budget of a software system, thus improving the maintainability of software is important for both the software industry and its customers. Program comprehension is the initial step of software maintenance that requires the major amount of maintenance’s time and effort. We conjuncture that developers’ gender and the type of representations that developers utilize to perform program comprehension impact their efficiency and effectiveness. These factors must be considered and carefully studied, because they may hide some significant effects to be found by analyzing the comprehension process. In this dissertation, inspired by the literature on the usefulness of eye-trackers to study the cognitive process involved in problem solving activities, we perform a mapping study and evaluate all research relevant to the use of eye-tracking technique in software engineering. The results motivate us to perform a set of eye-tracking studies to analyze the impact of two important factors on program comprehension: representation type (textual vs. graphical) and developers’ gender. Moreover, we investigate and compare viewing strategies variability amongst developers to understand how the representation type and gender differences influence viewing strategies. Overall, our results indicate that structured text provides more cognitive support for developers while performing program comprehension with small systems compared to a graphical representation. Developers spend less time and effort working with textual representations. However, developers mostly preferred to use graphical representations and our results confirm that the topological structure of graphical representations helps developers to memorize the location of the elements and to find the relevant ones faster in comparison with textual representation. Moreover, the hierarchical structure of the representation guides developers to follow specific viewing strategies while working with representations. Regarding the impact of gender, our results emphasize that male and female developers exploit different viewing strategies while reading source code or recalling the names of identifiers. Female developers seem to carefully weigh all options and rule out wrong answers, while male developers seem to quickly set their minds on some answers and move forward. Moreover, female developers spend more time on each source code entity and analyze it before going to the next one. In contrast, male developers utilize several attention switching strategies between different source code entities

Development of a Sensorized Timber Processor Head Prototype – Part 1: Sensors Description and Hardware Integration

Forest operations are in constant development to provide increasingly higher standards of economic and environmental sustainability. The latest innovation trends are concentrated in the generation, storage and management of data related to the harvesting process, timber products and logistics operations. Current technologies provide productivity and position, but only physical parameters are made available for timber products. The possibility of providing a comprehensive quality evaluation of roundwood early in the supply chain and linking the information to each log provides a new tool for optimization of the whole forest-timber supply chain. Current in-field methods for grading logs are based on visual rating scales, which are subjective, operator-dependent and time-consuming. As an alternative, a sensorized processor head was developed, featuring the following sensors: near infrared (NIR) spectrometer and hyperspectral cameras to identify surface defects, stress wave and time of flight sensors to estimate timber density, hydraulic flow sensor to estimate cross-cutting resistance and delimbing sensors to estimate branches number and approximate position. The prototype also deployed an RFID UHF system, which allowed the identification of the incoming tree and individually marked each log, relating the quality parameters recorded to the physical item and tracing it along the supply chain. The tested sensors were installed and designed to be independent, nevertheless, their integrated use provides a comprehensive evaluation of timber quality. This paper presents the technical solutions adopted, the main hindrances found and some preliminary results of the operative prototype as tested in laboratory and in forest operational conditions