Software engineering whispers: The effect of textual vs. graphical software design descriptions on software design communication

Context:\ua0Software\ua0engineering\ua0is a social and collaborative activity. Communicating and sharing knowledge between\ua0software\ua0developers requires much effort. Hence, the quality of\ua0communication\ua0plays an important role in influencing project success. To better understand the\ua0effect\ua0of\ua0communication\ua0on project success, more in-depth empirical studies investigating this phenomenon are needed. Objective: We investigate the\ua0effect\ua0of using a\ua0graphical\ua0versus\ua0textual\ua0design\ua0description\ua0on co-located\ua0software\ua0design\ua0communication. Method: Therefore, we conducted a family of experiments involving a mix of 240\ua0software\ua0engineering\ua0students from four universities. We examined how different\ua0design\ua0representations (i.e.,\ua0graphical\ua0vs.\ua0textual) affect the ability to Explain, Understand, Recall, and Actively Communicate knowledge. Results: We found that the\ua0graphical\ua0design\ua0description\ua0is better than the\ua0textual\ua0in promoting Active Discussion between developers and improving the Recall of\ua0design\ua0details. Furthermore, compared to its unaltered version, a well-organized and motivated\ua0textual\ua0design\ua0description–that is used for the same amount of time–enhances the recall of\ua0design\ua0details and increases the amount of active discussions at the cost of reducing the perceived quality of explaining

Investigating styles in variability modeling: Hierarchical vs. constrained styles

Context: A common way to represent product lines is with variability modeling. Yet, there are different ways to extract and organize relevant characteristics of variability. Comprehensibility of these models and the ease of creating models are important for the efficiency of any variability management approach. Objective: The goal of this paper is to investigate the comprehensibility of two common styles to organize variability into models - hierarchical and constrained - where the dependencies between choices are specified either through the hierarchy of the model or as cross-cutting constraints, respectively. Method: We conducted a controlled experiment with a sample of 90 participants who were students with prior training in modeling. Each participant was provided with two variability models specified in Common Variability Language (CVL) and was asked to answer questions requiring interpretation of provided models. The models included 9 to 20 nodes and 8 to 19 edges and used the main variability elements. After answering the questions, the participants were asked to create a model based on a textual description. Results: The results indicate that the hierarchical modeling style was easier to comprehend from a subjective point of view, but there was also a significant interaction effect with the degree of dependency in the models, that influenced objective comprehension. With respect to model creation, we found that the use of a constrained modeling style resulted in higher correctness of variability models. Conclusions: Prior exposure to modeling style and the degree of dependency among elements in the model determine what modeling style a participant chose when creating the model from natural language descriptions. Participants tended to choose a hierarchical style for modeling situations with high dependency and a constrained style for situations with low dependency. Furthermore, the degree of dependency also influences the comprehension of the variability model

Effects of variability in models: a family of experiments

The ever-growing need for customization creates a need to maintain software systems in many different variants. To avoid having to maintain different copies of the same model, developers of modeling languages and tools have recently started to provide implementation techniques for such variant-rich systems, notably variability mechanisms, which support implementing the differences between model variants. Available mechanisms either follow the annotative or the compositional paradigm, each of which have dedicated benefits and drawbacks. Currently, language and tool designers select the used variability mechanism often solely based on intuition. A better empirical understanding of the comprehension of variability mechanisms would help them in improving support for effective modeling. In this article, we present an empirical assessment of annotative and compositional variability mechanisms for three popular types of models. We report and discuss findings from a family of three experiments with 164 participants in total, in which we studied the impact of different variability mechanisms during model comprehension tasks. We experimented with three model types commonly found in modeling languages: class diagrams, state machine diagrams, and activity diagrams. We find that, in two out of three experiments, annotative technique lead to better developer performance. Use of the compositional mechanism correlated with impaired performance. For all three considered tasks, the annotative mechanism was preferred over the compositional one in all experiments. We present actionable recommendations concerning support of flexible, tasks-specific solutions, and the transfer of established best practices from the code domain to models

Exploring cognitive style and task-specific preferences for process representations

Process models describe someone's understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area

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

Do Models Improve the Understanding of Safety Compliance Needs?: Insights from a Pilot Experiment

Context. Many critical systems must meet safety compliance needs from safety standards. These standards are usually large textual documents whose compliance needs can be hard to understand. As a solution, the use of models has been proposed. Goal. We aim to provide evidence of the extent to which models improve the understanding of safety compliance needs. Method. We designed an experiment and ran a pilot to study the effectiveness, efficiency, and perceived benefits of understanding these needs, with the text of standards and with models in the form of UML object diagrams. Results. The overall results from 15 Bachelor students show that the effectiveness of understanding safety compliance needs increases very little with models (2%), and the efficiency even decreases (24%). Nonetheless, the results improve when the potential complexity in navigating the models is taken into account (15% effectiveness increase). The students find benefits in using the models but most consider that the models are hard to understand. Conclusions. The extent to which models improve the understanding of safety compliance needs seems to be lower than what the research community expects. New studies are necessary to confirm our initial insights.European CommissionThe research leading to this paper has received funding from the AMASS project (H2020-ECSEL grant agreement no 692474; Spain’s MINECO ref. PCIN-2015-262) and the AMoDDI project (Ref. 11130583)

An experimental evaluation of the understanding of safety compliance needs with models

Proceedings of: 36th International Conference on Conceptual Modeling, ER 2017, Valencia, Spain, November 6–9, 2017Context: Most safety-critical systems have to fulfil compliance needs specified in safety standards. These needs can be difficult to understand from the text of the standards, and the use of conceptual models has been proposed as a solution. Goal: We aim to evaluate the understanding of safety compliance needs with models. Method: We have conducted an experiment to study the effectiveness, efficiency, and perceived benefits in understanding these needs, with text of safety standards and with UML object diagrams. Results: Sixteen Bachelor students participated in the experiment. Their average effectiveness in understanding compliance needs and their average efficiency were higher with models (17% and 15%, respectively). However, the difference is not statistically significant. The students found benefits in using models, but on average they are undecided about their ease of understanding. Conclusions: Although the results are not conclusive enough, they suggest that the use of models could improve the understanding of safety compliance needs.The research leading to this paper has received funding from the AMASS project (H2020-ECSEL grant agreement no 692474; Spain’s MINECO ref. PCIN-2015-262) and the AMoDDI project (Ref. 11130583). We also thank the subjects that participated in the experiment

Fifty years of the Psychology of Programming

This paper reflects on the evolution (past, present and future) of the ‘psychology of programming' over the 50 year period of this anniversary issue. The International Journal of Human-Computer Studies (IJHCS) has been a key venue for much seminal work in this field, including its first foundations, and we review the changing research concerns seen in publications over these five decades. We relate this thematic evolution to research taking place over the same period within more specialist communities, especially the Psychology of Programming Interest Group (PPIG), the Empirical Studies of Programming series (ESP), and the ongoing community in Visual Languages and Human-Centric Computing (VL/HCC). Many other communities have interacted with psychology of programming, both influenced by research published within the specialist groups, and in turn influencing research priorities. We end with an overview of the core theories that have been developed over this period, as an introductory resource for new researchers, and also with the authors’ own analysis of key priorities for future research

A systematic characterization of cognitive techniques for learning from textual and pictorial representations

Pictorial representations can play a pivotal role in both printed and digital learning material. Although there has been extensive research on cognitive techniques and strategies for learning from text, the same cannot be said for static and dynamic pictorial representations. In this paper we propose a systematic characterization of cognitive learning techniques that is founded on both theoretical and empirical research. The characterization relates the learning techniques to classes of cognitive processes as well as to textual and pictorial representations. We show how successful strategies for learning from both plain text and illustrated text are covered by the characterization. We also exemplify how the construction of new strategies for pictorial representations can be informed by the characterization

A Framework for Seamless Variant Management and Incremental Migration to a Software Product-Line

Context: Software systems often need to exist in many variants in order to satisfy varying customer requirements and operate under varying software and hardware environments. These variant-rich systems are most commonly realized using cloning, a convenient approach to create new variants by reusing existing ones. Cloning is readily available, however, the non-systematic reuse leads to difficult maintenance. An alternative strategy is adopting platform-oriented development approaches, such as Software Product-Line Engineering (SPLE). SPLE offers systematic reuse, and provides centralized control, and thus, easier maintenance. However, adopting SPLE is a risky and expensive endeavor, often relying on significant developer intervention. Researchers have attempted to devise strategies to synchronize variants (change propagation) and migrate from clone&own to an SPL, however, they are limited in accuracy and applicability. Additionally, the process models for SPLE in literature, as we will discuss, are obsolete, and only partially reflect how adoption is approached in industry. Despite many agile practices prescribing feature-oriented software development, features are still rarely documented and incorporated during actual development, making SPL-migration risky and error-prone.Objective: The overarching goal of this PhD is to bridge the gap between clone&own and software product-line engineering in a risk-free, smooth, and accurate manner. Consequently, in the first part of the PhD, we focus on the conceptualization, formalization, and implementation of a framework for migrating from a lean architecture to a platform-based one.Method: Our objectives are met by means of (i) understanding the literature relevant to variant-management and product-line migration and determining the research gaps (ii) surveying the dominant process models for SPLE and comparing them against the contemporary industrial practices, (iii) devising a framework for incremental SPL adoption, and (iv) investigating the benefit of using features beyond PL migration; facilitating model comprehension.Results: Four main results emerge from this thesis. First, we present a qualitative analysis of the state-of-the-art frameworks for change propagation and product-line migration. Second, we compare the contemporary industrial practices with the ones prescribed in the process models for SPL adoption, and provide an updated process model that unifies the two to accurately reflect the real practices and guide future practitioners. Third, we devise a framework for incremental migration of variants into a fully integrated platform by exploiting explicitly recorded metadata pertaining to clone and feature-to-asset traceability. Last, we investigate the impact of using different variability mechanisms on the comprehensibility of various model-related tasks.Future work: As ongoing and future work, we aim to integrate our framework with existing IDEs and conduct a developer study to determine the efficiency and effectiveness of using our framework. We also aim to incorporate safe-evolution in our operators