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Unburdening Onboarding in Software Product Lines
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The IT sector struggles with talent acquisition and low retention rates. While several field studies have explored onboarding of software developers, the software engineering literature lacks studies that develop and evaluate theoretical models. This study seeks to explore the link between onboarding of new hires and turnover intention of these professionals. In particular, we develop a theoretical model that identifies a number of onboarding activities, and link these to onboarding success. We then look at what we have termed “organizational fit,” which we define as two aspects of software professionals, namely job satisfaction and the quality of their relationships on the workfloor, and investigate how these mediate the relation between short-term onboarding success and a longer-term intention to leave (or stay with) an organization. We test our model with a sample of 102 software professionals using PLS-SEM. The findings suggest that providing support to new hires plays a major role in onboarding success, but that training is less important. Further, we found that job satisfaction mediates the relationship between onboarding success and turnover intention, but workplace relationship quality does not. Based on the findings, we discuss a number of implications for practice and suggestions for future research.
New employees are supposed to quickly understand their tasks, internal processes and familiarize with colleagues. This process is called “onboarding” and is still mainly realized by organizational methods from human resource management, such as introductory events or special employee sessions. Software tools and especially mobile applications are an innovative means to support provide onboarding processes in a modern, even remote, way. In this paper we analyze how the use of gamification can enhance onboarding processes. Firstly, we describe a mobile onboarding application specifically developed for the young, technically literate generations Y and Z, who are just about to start their career. Secondly, we report on a study with 98 students and young employees. We found that participants enjoyed the gamified application. They especially appreciated the feature “Team Bingo” which facilitates social integration and teambuilding. Based on the OCEAN personality model (“Big Five”), the personality traits agreeableness and openness revealed significant correlations with a preference for the gamified onboarding application.
Newcomers’ seamless onboarding is important for open collaboration communities, particularly those that leverage outsiders’ contributions to remain sustainable. Nevertheless, previous work shows that OSS newcomers often face several barriers to contribute, which lead them to lose motivation and even give up on contributing. A well-known way to help newcomers overcome initial contribution barriers is mentoring. This strategy has proven effective in offline and online communities, and to some extent has been employed in OSS projects. Studying mentors’ perspectives on the barriers that newcomers face play a vital role in improving onboarding processes; yet, OSS mentors face their own barriers, which hinder the effectiveness of the strategy. Since little is known about the barriers mentors face, in this paper, we investigate the barriers that affect mentors and their newcomer mentees. We interviewed mentors from OSS projects and qualitatively analyzed their answers. We found 44 barriers: 19 that affect mentors; and 34 that affect newcomers (9 affect both newcomers and mentors). Interestingly, most of the barriers we identified (66%) have a social nature. Additionally, we identified 10 strategies that mentors indicated to potentially alleviate some of the barriers. Since gender-related challenges emerged in our analysis, we conducted nine follow-up structured interviews to further explore this perspective. The contributions of this paper include: identifying the barriers mentors face; bringing the unique perspective of mentors on barriers faced by newcomers; unveiling strategies that can be used by mentors to support newcomers; and investigating gender-specific challenges in OSS mentorship. Mentors, newcomers, online communities, and educators can leverage this knowledge to foster new contributors to OSS projects.
Community-based Open Source Software (OSS) projects are usually self-organized and dynamic, receiving contributions from distributed volunteers. Newcomer are important to the survival, long-term success, and continuity of these communities. However, newcomers face many barriers when making their first contribution to an OSS project, leading in many cases to dropouts. Therefore, a major challenge for OSS projects is to provide ways to support newcomers during their first contribution. In this paper, we propose and evaluate FLOSScoach, a portal created to support newcomers to OSS projects. FLOSScoach was designed based on a conceptual model of barriers created in our previous work. To evaluate the portal, we conducted a study with 65 students, relying on qualitative data from diaries, self-efficacy questionnaires, and the Technology Acceptance Model. The results indicate that FLOSScoach played an important role in guiding newcomers and in lowering barriers related to the orientation and contribution process, whereas it was not effective in lowering technical barriers. We also found that FLOSScoach is useful, easy to use, and increased newcomers' confidence to contribute. Our results can help project maintainers on deciding the points that need more attention in order to help OSS project newcomers overcome entry barriers.
The recent and fast expansion of OSS (Open-source software) communities has fostered research on how open source projects evolve and how their communities interact. Several research studies show that the inflow of new developers plays an important role in the longevity and the success of OSS projects. Beside that they also discovered that an high percentage of newcomers tend to leave the project because of the socio-technical barriers they meet when they join the project. However, such research effort did not generate yet concrete results in support retention and training of project newcomers. In this thesis dissertation we investigated problems arising when newcomers join software projects, and possible solutions to support them. Specifically, we studied (i) how newcomers behave during development activities and how they interact with others developers with the aim at (ii) developing tools and/or techniques for supporting them during the integration in the development team. Thus, among the various recommenders, we defined (i) a tool able to suggest appropriate mentors to newcomers during the training stage; then, with the aim at supporting newcomers during program comprehension we defined other two recommenders: a tool that (ii) generates high quality source code summaries and another tool able to (iii) provide descriptions of specific source code elements. For future work, we plan to improve the proposed recommenders and to integrate other kind of recommenders to better support newcomers in OSS projects.
Numerous open source software projects are based on volunteers collaboration and require a continuous influx of newcomers for their continuity. Newcomers face barriers that can lead them to give up. These barriers hinder both developers willing to make a single contribution and those willing to become a project member.Objective
This study aims to identify and classify the barriers that newcomers face when contributing to open source software projects.Method
We conducted a systematic literature review of papers reporting empirical evidence regarding the barriers that newcomers face when contributing to open source software (OSS) projects. We retrieved 291 studies by querying 4 digital libraries. Twenty studies were identified as primary. We performed a backward snowballing approach, and searched for other papers published by the authors of the selected papers to identify potential studies. Then, we used a coding approach inspired by open coding and axial coding procedures from Grounded Theory to categorize the barriers reported by the selected studies.ResultsWe identified 20 studies providing empirical evidence of barriers faced by newcomers to OSS projects while making a contribution. From the analysis, we identified 15 different barriers, which we grouped into five categories: social interaction, newcomers’ previous knowledge, finding a way to start, documentation, and technical hurdles. We also classified the problems with regard to their origin: newcomers, community, or product.Conclusion
The results are useful to researchers and OSS practitioners willing to investigate or to implement tools to support newcomers. We mapped technical and non-technical barriers that hinder newcomers’ first contributions. The most evidenced barriers are related to socialization, appearing in 75% (15 out of 20) of the studies analyzed, with a high focus on interactions in mailing lists (receiving answers and socialization with other members). There is a lack of in-depth studies on technical issues, such as code issues. We also noticed that the majority of the studies relied on historical data gathered from software repositories and that there was a lack of experiments and qualitative studies in this area.
Concept Mapping in Middle School Mathematics (CMMSM) is a developing mathematics intervention designed to increase the likelihood of success in Algebra I for sixth-, seventh-, and eighth-grade students from low-income neighborhoods. The intervention uses concept mapping,to strengthen teacher understanding of the connections among,mathematical,concepts and to bring meaningful,learning of connected ,concepts to their classrooms. This study details the progress of one ,group of 17 teachers as they learn to use CmapTools™ to connect ideas in planning instruction using activities and textbook units. 1,Introduction: Teaching and Learning Algebra I Algebra I is a gatekeeper subject for the secondary mathematics,curriculum. According to the American Association
A typical entry point for new developers in an open source project is to contribute a bug fix. However, finding an appropriate bug and an appropriate fix for that bug requires a good understanding of the project, which is nontrivial. Here, we extend Tesseract - an interactive project exploration environment - to allow new developers to search over bug descriptions in a project to quickly identify and explore bugs of interest and their related resources. More specifically, we extended Tesseract with search capabilities that enable synonyms and similar-bugs search over bug descriptions in a bug repository. The goal is to enable users to identify bugs of interest, resources related to that bug, (e.g., related files, contributing developers, communication records), and visually explore the appropriate socio-technical dependencies for the selected bug in an interactive manner. Here we present our search extension to Tesseract.
A newcomer to a software project must typically come up-to-speed on a large, varied amount of information about the project before becoming productive. Assimilating this information in the open-source context is difficult because a newcomer cannot rely on the mentoring approach that is commonly used in traditional software developments. To help a newcomer to an open-source project become productive faster, we propose Hipikat, a tool that forms an implicit group memory from the information stored in a project's archives, and that recommends artifacts from the archives that are relevant to a task that a newcomer is trying to perform. To investigate this approach, we have instantiated the Hipikat tool for the Eclipse open-source project. In this paper we describe the Hipikat tool, we report on a qualitative study conducted with a Hipikat mock-up on a medium-sized in-house project, and we report on a case study in which Hipikat recommendations were evaluated for a task on Eclipse.
How do new college graduates experience their first software development jobs? In what ways are they prepared by their educational experiences, and in what ways do they struggle to be productive in their new positions? We report on a "fly-on-the-wall" observational study of eight recent college graduates in their first six months of a software development position at Microsoft Corporation. After a total of 85 hours of on-the-job observation, we report on the common abilities evidenced by new software developers including how to program, how to write design specifications, and evidence of persistence strategies for problem-solving. We also classify some of the common ways new software developers were observed getting stuck: communication, collaboration, technical, cognition, and orientation. We report on some common misconceptions of new developers which often frustrate them and hinder them in their jobs, and conclude with recommendations to align Computer Science curricula with the observed needs of new professional developers.
A fundamental reason for investing in product families is to minimize the application engineering costs. Several organizations that employ product families, however, are becoming increasingly aware of the fact that, despite the efforts in domain engineering, deriving individual products from their shared software assets is a time and effort consuming activity. In this paper we present a collection of product derivation problems that we identified during a case study at two large and mature industrial organizations. These problems are attributed to the lack of methodological support for application engineering, and to underlying causes of complexity and implicit properties. For each problem, we provide a description and an example, while for each cause we present a description, consequences, solutions and research issues. The discussions in this paper are relevant outside the context of the two companies, as the challenges they face do or eventually will arise in, for example, comparable or less mature organizations.
Concept maps are graphical tools for organizing and representing knowledge. They include concepts, usually enclosed in circles or boxes of some type, and relationships between concepts indicated by a connecting line linking two concepts. Words on the line, referred to as linking words or linking phrases, specify the relationship between the two concepts. We define concept as a perceived regularity in events or objects, or records of events or objects, designated by a label. The label for most concepts is a word, although sometimes we use symbols such as + or %, and sometimes more than one word is used. Propositions are statements about some object or event in the universe, either naturally occurring or constructed. Propositions contain two or more concepts connected using linking words or phrases to form a meaningful statement. Sometimes these are called semantic units, or units of meaning. Figure 1 shows an example of a concept map that describes the structure of concept maps and illustrates the above characteristics.
Online resources can help novice developers learn basic programming skills, but few resources support progressing from writing working code to learning professional web development practices. We address this gap by advancing Readily Available Learning Experiences, a conceptual approach for transforming all professional web applications into opportunities for authentic learning. This article presents Isopleth, a web-based platform that helps learners make sense of complex code constructs and hidden asynchronous relationships in professional web code. Isopleth embeds sensemaking scaffolds informed by the learning sciences to (1) expose hidden functional and event-driven relationships, (2) surface functionally related slices of code, and (3) support learners manipulating the provided code representations. To expose event-driven relationships, Isopleth implements a novel technique called Serialized Deanonymization to determine and visualize asynchronous functional relationships. To evaluate Isopleth, we conducted a case study across 12 professional websites and a user study with 14 junior and senior developers. Results show that Isopleth’s sensemaking scaffolds helped to surface implementation approaches in event binding, web application design, and complex interactive features across a range of complex professional web applications. Moreover, Isopleth helped junior developers improve the accuracy of their conceptual models of how features are implemented by 31% on average.
This extended abstract summarizes our article entitled "Teaching Software Product Lines: A Snapshot of Current Practices and Challenges" published in the ACM Transactions on Computing Education, vol. 18 in 2017 (http://doi.acm.org/10.1145/3088440). The article reports on three initiatives we have conducted with scholars, educators, industry practitioners, and students to understand the connection between software product lines and education and to derive recommendations for educators to continue improving the state of practice of teaching SPLs.
Software Product Line (SPL) engineering has emerged to provide the means to efficiently model, produce, and maintain multiple similar software variants, exploiting their common properties, and managing their variabilities (differences). With over two decades of existence, the community of SPL researchers and practitioners is thriving, as can be attested by the extensive research output and the numerous successful industrial projects. Education has a key role to support the next generation of practitioners to build highly complex, variability-intensive systems. Yet, it is unclear how the concepts of variability and SPLs are taught, what are the possible missing gaps and difficulties faced, what are the benefits, and what is the material available. Also, it remains unclear whether scholars teach what is actually needed by industry. In this article, we report on three initiatives we have conducted with scholars, educators, industry practitioners, and students to further understand the connection between SPLs and education, that is, an online survey on teaching SPLs we performed with 35 scholars, another survey on learning SPLs we conducted with 25 students, as well as two workshops held at the International Software Product Line Conference in 2014 and 2015 with both researchers and industry practitioners participating. We build upon the two surveys and the workshops to derive recommendations for educators to continue improving the state of practice of teaching SPLs, aimed at both individual educators as well as the wider community.
Software companies regularly recruit skilled and talented employees to meet evolving business requirements. Although companies expect early contributions, new hires often take several weeks to reach the same productivity level as existing employees. We refer to this transition of new hires from novices to experts as ramp-up journey. There can be various factors such as lack of technical skills or lack of familiarity with the process that influence the ramp-up journey of new hires. The goal of our work is to identify those factors and study their influence on the ramp-up journey. We expect the results from this study to help identify the need of various types of assistance to new hires to ramp-up faster. As a first step towards our goal, this paper explores the impact of two strategic practices, namely distributed development and internship on the ramp-up journey of new hires. Our results show that new hires in proximity to the core development team and new hires with prior internship experience perform better than others in the beginning. In the overall ramp-up journey, the effect of the two factors attenuates, yet nevertheless better compared to their counterparts. Product teams can use this information to pay special attention to non-interns and use better tools for distributed, cooperative work to help new hires ramp-up faster.
The success of product line engineering techniques in the last 15 years has popularized the use of software variability as a key modeling approach for describing the commonality and variability of systems at all stages of the software lifecycle. Software product lines enable a family of products to share a common core platform, while allowing for product specific functionality being built on top of the platform. Many companies have exploited the concept of software product lines to increase the resources that focus on highly differentiating functionality and thus improve their competitiveness with higher quality and reusable products and decreasing the time-to-market condition. Many books on product line engineering either introduce specific product line techniques or include brief summaries of industrial cases. From these sources, it is difficult to gain a comprehensive understanding of the various dimensions and aspects of software variability. Here the editors address this gap by providing a comprehensive reference on the notion of variability modeling in the context of software product line engineering, presenting an overview of the techniques proposed for variability modeling and giving a detailed perspective on software variability management. Their book is organized in four main parts, which guide the reader through the various aspects and dimensions of software variability. Part 1 which is mostly written by the editors themselves introduces the major topics related to software variability modeling, thus providing a multi-faceted view of both technological and management issues. Next, part 2 of the book comprises four separate chapters dedicated to research and commercial tools. Part 3 then continues with the most practical viewpoint of the book presenting three different industry cases on how variability is managed in real industry projects. Finally, part 4 concludes the book and encompasses six different chapters on emerging research topics in software variability like e.g. service-oriented or dynamic software product lines, or variability and aspect orientation. Each chapter briefly summarizes “What you will learn in this chapter”, so both expert and novice readers can easily locate the topics dealt with. Overall, the book captures the current state of the art and best practices, and indicates important open research challenges as well as possible pitfalls. Thus it serves as a reference for researchers and practitioners in software variability management, allowing them to develop the next set of solutions, techniques and methods in this complicated and yet fascinating field of software engineering.
The notion of features is commonly used to describe the functional and non-functional characteristics of a system. In software product line engineering, features often become the prime entities of software reuse and are used to distinguish the individual products of a product line. Properly decomposing a product line into features, and correctly using features in all engineering phases, is core to the immediate and long-term success of such a system. Yet, although more than ten different definitions of the term feature exist, it is still a very abstract concept. Definitions lack concrete guidelines on how to use the notion of features in practice.
To address this gap, we present a qualitative empirical study on actual feature usage in industry. Our study covers three large companies and an in-depth, contextualized analysis of 23 features, perceived by the interviewees as typical, atypical (outlier), good, or bad representatives of features. Using structured interviews, we investigate the rationales that lead to a feature's perception, and identify and analyze core characteristics (facets) of these features. Among others, we find that good features precisely describe customer-relevant functionality, while bad features primarily arise from rashly executed processes. Outlier features, serving unusual purposes, are necessary, but do not require the full engineering process of typical features.
This book is an introductory text on design science, intended to support both graduate students and researchers in structuring, undertaking and presenting design science work. It builds on established design science methods as well as recent work on presenting design science studies and ethical principles for design science, and also offers novel instruments for visualizing the results, both in the form of process diagrams and through a canvas format. While the book does not presume any prior knowledge of design science, it provides readers with a thorough understanding of the subject and enables them to delve into much deeper detail, thanks to extensive sections on further reading. Design science in information systems and technology aims to create novel artifacts in the form of models, methods, and systems that support people in developing, using and maintaining IT solutions. This work focuses on design science as applied to information systems and technology, but it also includes examples from, and perspectives of, other fields of human practice. Chapter 1 provides an overview of design science and outlines its ties with empirical research. Chapter 2 discusses the various types and forms of knowledge that can be used and produced by design science research, while Chapter 3 presents a brief overview of common empirical research strategies and methods. Chapter 4 introduces a methodological framework for supporting researchers in doing design science research as well as in presenting their results. This framework includes five core activities, which are described in detail in Chapters 5 to 9. Chapter 10 discusses how to communicate design science results, while Chapter 11 compares the proposed methodological framework with methods for systems development and shows how they can be combined. Chapter 12 discusses how design science relates to research paradigms, in particular to positivism and interpretivism. Lastly, Chapter 13 discusses ethical issues and principles for design science research. © Springer International Publishing Switzerland 2014. All rights are reserved.
The experiment data from the operation is input to the analysis and interpretation. After collecting experimental data in the operation phase, we want to be able to draw conclusions based on this data. To be able to draw valid conclusions, we must interpret the experiment data.
The notion of scaffolding learners to help them succeed in solving problems otherwise too difficult for them is an important idea that has extended into the design of scaffolded software tools for learners. However, although there is a growing body of work on scaffolded tools, scaffold design, and the impact of scaffolding, the field has not yet converged on a common theoretical framework that defines rationales and approaches to guide the design of scaffolded tools. In this article, we present a scaffolding design framework addressing scaffolded software tools for science inquiry. Developed through iterative cycles of inductive and theory-based analysis, the framework synthesizes the work of prior design efforts, theoretical arguments, and empirical work in a set of guidelines that are organized around science inquiry practices and the challenges learners face in those practices. The framework can provide a basis for developing a theory of pedagogical support and a mechanism to describe successful scaffolding approaches. It can also guide design, not in a prescriptive manner but by providing designers with heuristics and examples of possible ways to address the challenges learners face.
Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets.
Newcomers in a software development project often need assistance to complete their first tasks. Then a mentor, an experienced member of the team, usually teaches the newcomers what they need to complete their tasks. But, to allocate an experienced member of a team to teach a newcomer during a long time is neither always possible nor desirable, because the mentor could be more helpful doing more important tasks. During the development the team interacts with a version control system, bug tracking and mailing lists, and all these tools record data creating the project memory. Recommender systems can use the project memory to help newcomers in some tasks answering their questions, thus in some cases the developers do not need a mentor. In this paper we present Mentor, a recommender system to help newcomers to solve change requests. Mentor uses the Prediction by Partial Matching (PPM) algorithm and some heuristics to analyze the change requests, and the version control data, and recommend potentially relevant source code that will help the developer in the change request solution. We did three experiments to compare the PPM algorithm with the Latent Semantic Indexing (LSI). Using PPM we achieved results for recall rate between 37% and 66.8%, and using LSI the results were between 20.3% and 51.6%.
Introduction Design of the Case Study Data Collection Data Analysis Reporting and Dissemination Lessons Learned
From our experience with several organizations that employ software product families, we have learned that, contrary to popular belief, deriving individual products from shared software assets is a time-consuming and expensive activity. In this paper we therefore present a study that investigated the source of those problems. We provide the reader with a framework of terminology and concepts regarding product derivation. In addition, we present several problems and issues we identified during a case study at two large industrial organizations that are relevant to other, for example, comparable or less mature organizations.
Product line practices are increasingly becoming popular in the domain of embedded software systems. This paper presents results of assessing success, consistency, and quality of Testo's product line of climate and flue gas measurement devices after its construction and the delivery of three commercial products. The results of the assessment showed that the incremental introduction of architecture-centric product line development can be considered successful even though there is no quantifiable reduction of time-to-market as well as development and maintenance costs so far. The success is mainly shown by the ability of Testo to develop more complex products and the satisfaction of the involved developers. A major issue encountered is ensuring the quality of reusable components and the conformance of the products to the architecture during development and maintenance
The aim of this book is to present a critical synthesis of research in the field commonly known as the psychology of programming. This covers the activities involved in the different programming tasks, such as analysis, design, coding and maintenance. The purpose of such research is to provide ergonomic recommenda-tions for the specification of development environments and programming languages, as well as training and education aids. More generally, we are interested in providing experts with aids to problem-solving.
This meta-analysis reviews experimental and quasi-experimental studies in which students learned by constructing, modifying, or viewing node-link diagrams. Following an exhaustive search for studies meeting specified design criteria, 67 standardized mean difference effect sizes were extracted from 55 studies involving 5,818 participants. Students at levels ranging from Grade 4 to postsecondary used concept maps to learn in domains such as science, psychology, statistics, and nursing. Posttests measured recall and transfer. Across several instructional conditions, settings, and methodological features, the use of concept maps was associated with increased knowledge retention. Mean effect sizes varied from small to large depending on how concept maps were used and on the type of comparison treatment. Significant heterogeneity was found in most subsets.
Like other sciences and engineering disciplines, software engineering requires a cycle of model building, experimentation, and learning. Experiments are valuable tools for all software engineers who are involved in evaluating and choosing between different methods, techniques, languages and tools. The purpose of Experimentation in Software Engineering is to introduce students, teachers, researchers, and practitioners to empirical studies in software engineering, using controlled experiments. The introduction to experimentation is provided through a process perspective, and the focus is on the steps that we have to go through to perform an experiment. The book is divided into three parts. The first part provides a background of theories and methods used in experimentation. Part II then devotes one chapter to each of the five experiment steps: scoping, planning, execution, analysis, and result presentation. Part III completes the presentation with two examples. Assignments and statistical material are provided in appendixes. Overall the book provides indispensable information regarding empirical studies in particular for experiments, but also for case studies, systematic literature reviews, and surveys. It is a revision of the authors' book, which was published in 2000. In addition, substantial new material, e.g. concerning systematic literature reviews and case study research, is introduced. The book is self-contained and it is suitable as a course book in undergraduate or graduate studies where the need for empirical studies in software engineering is stressed. Exercises and assignments are included to combine the more theoretical material with practical aspects. Researchers will also benefit from the book, learning more about how to conduct empirical studies, and likewise practitioners may use it as a "cookbook" when evaluating new methods or techniques before implementing them in their organization. © Springer-Verlag Berlin Heidelberg 2012. All rights are reserved.
This is the first empirical study of the use of the C macro preprocessor, Cpp. To determine how the preprocessor is used in practice, this paper analyzes 26 packages comprising 1.4 million lines of publicly available C code. We determine the incidence of C preprocessor usage-whether in macro definitions, macro uses, or dependences upon macros-that is complex, potentially problematic, or inexpressible in terms of other C or C++ language features. We taxonomize these various aspects of preprocessor use and particularly note data that are material to the development of tools for C or C++, including translating from C to C++ to reduce preprocessor usage. Our results show that, while most Cpp usage follows fairly simple patterns, an effective program analysis tool must address the preprocessor. The intimate connection between the C programming language and Cpp, and Cpp's unstructured transformations of token streams often hinder both programmer understanding of C programs and tools built to engineer C programs, such as compilers, debuggers, call graph extractors, and translators. Most tools make no attempt to analyze macro usage, but simply preprocess their input, which results in a number of negative consequences; an analysis that takes Cpp into account is preferable, but building such tools requires an understanding of actual usage. Differences between the semantics of Cpp and those of C can lead to subtle bugs stemming from the use of the preprocessor, but there are no previous reports of the prevalence of such errors. Use of C++ can reduce some preprocessor usage, but such usage has not been previously measured. Our data and analyses shed light on these issues and others related to practical understanding or manipulation of real C programs. The results are of interest to language designers, tool writers, programmers, and software engineers.
Software product lines - practices and patterns
- P Clements
- L M Northrop
What is a feature?: a qualitative study of features in industrial software product lines
- T Berger
- D Lettner
- J Rubin
- P Grünbacher
- A Silva
- M Becker