Unifying Theories of Program Comprehension

This paper describes the architecture of an ongoing developed Flexible and Extensible Program comprehension Support System (FEPSS). Program comprehension is a process for supporting users to understand a software system and is required for forward, reverse engineering, and re-engineering. In a program comprehension process some artifacts such as control-flow graphs and data-flow graphs need to be generated to help users in understanding and some artifacts such as design rationale and testing cases also need to be recorded for further re-engineering. It is impossible to predict all the types of artifacts to be supported in a program comprehension process as at least no one makes a re-engineering plan when designing a new software system. Therefore a program comprehension system should support a great flexibility and extensibility in artifact modelling. The approach which FEPSS takes is to provide a support system where the users can add new artifacts even at run time. FEPSS uses a relational object model to link the artifacts in order to integrate various activities in a program comprehension process as a whole. To help users to understand the artifacts and their relationships FEPSS uses 3D visualization to display artifacts. As the number of the artifacts for a software system can be very large FEPSS provides a navigation tool for users to control what artifacts to display. Based on these mechanisms, FEPSS can support users to analyze, model, and explore the artifacts through pictures.

We investigate the relative effectiveness of top‐down versus bottom‐up strategies for forecasting the demand of an item that belongs to a product family. The demand for each item in the family is assumed to follow a first‐order univariate autoregressive process. Under the top‐down strategy, the aggregate demand is forecasted by using the historical data of the family demand. The demand forecast for the items is then derived by proportional allocation of the aggregate forecast. Under the bottom‐up strategy, the demand forecast for each item is directly obtained by using the historical demand data of the particular item. In both strategies, the forecasting technique used is exponential smoothing. We analytically evaluate the condition under which one forecasting strategy is preferred over the other when the lag‐1 autocorrelation of the demand time series for all the items is identical. We show that when the lag‐1 autocorrelation is smaller than or equal to 1/3, the maximum difference in the performance of the two forecasting strategies is only 1%. However, if the lag‐1 autocorrelation of the demand for at least one of the items is greater than 1/3, then the bottom‐up strategy consistently outperforms the top‐down strategy, irrespective of the items' proportion in the family and the coefficient of correlation between the item demands. A simulation study reveals that the analytical findings hold even when the lag‐1 autocorrelation of the demand processes is not identical. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.

For more than 50 years, psychology has been dominated by a top-down research strategy in which a simplistic account of the hypothetico-deductive method is paired with null hypothesis testing in order to test hypotheses and theories. As a consequence of this focus on testing, psychologists have failed to pay sufficient attention to a complementary, bottom-up research strategy in which data-to-theory research is properly pursued.This bottom-up strategy has 2 primary aspects: the detection of phenomena, mostly in the form of empirical generalizations, and the subsequent understanding of those phenomena through the abductive generation of explanatory theories. This article provides a methodologically informative account of phenomena detection with reference to psychology. It begins by presenting the important distinctions between data, phenomena, and theory. It then identifies a number of different methodological strategies that are used to identify empirical phenomena. Thereafter, it discusses aspects of the nature of science that are prompted by a consideration of the distinction between data, phenomena, and explanatory theory. Taken together, these considerations press for significant changes in the way we think about and practice psychological research. The adoption of these changes would help psychology correct a number of its major current research deficiencies.

This special issue of EMSE comprises four selected papers on program comprehension, an unusually wide-ranging research area within software engineering. It encompasses the human activities of comprehending software, the technologies for supporting the program comprehension, and their evaluation. It is notable for combining aspects of cognitive psychology, tool design, and empirical study of users, tools, and software artifacts. In “High-MCC Functions in the Linux Kernel”, the authors challenge the assumption that functions with high values of McCabe Cyclomatic Complexity (MCC) are likely to be problematic to understand and maintain. They explore the nuances of what a high MCC value might mean, and examine functions from the Linux kernel with high MCC values. They find that often the structure of these functions is found to be easy to understand by users, leading to the conclusion that syntactic measures alone may not be a good metric for understandability. In “Measuring and Modeling Programming Experience”, the authors describe a controlled experiment in assessing how the experience of software developers can best be measured. This is a particularly important problem in program comprehension research, as developer experience is often a key factor that must be explicitly taken into account when studying how programmers perform problem-solving tasks. In “An Empirical Study on the Impact of Static Typing on Software Maintainability”, the authors tackle one of the classic programming language debates: How does static typing affect human software development capabilities? The authors approach this question from an empirical perspective. In a carefully designed experiment, the authors assess the impact of static typing on understanding undocumented code, fixing type errors, and fixing semantic errors. The results show rigorous empirical evidence that static types are indeed beneficial to these activities, except when fixing semantic errors. In “Labeling Source Code with Information Retrieval Methods: An Empirical Study”, the authors address augmenting source code with explanatory labels. Automated techniques for this exist, which are based on, e.g., Vector Space Models, Latent Semantic Indexing (LSI), and latent Dirichlet allocation (LDA). The authors describe an experiment in which such Empir Software Eng (2014) 19:1259–1260 DOI 10.1007/s10664-014-9329-5

Many organizations and open-source projects use the C preprocessor (CPP) to implement configurability in their software systems. Despite extensive research, existing studies on the effects of CPP use on program comprehension are still limited to experiences, opinions, and empirical studies with narrow scopes. So, it is unclear whether the CPP actually leads to what is sometimes referred to as "#ifdef hell." In this paper, we expand the existing evidence on program comprehension in the presence of CPP directives, but we also highlight a surprising dilemma. We conducted an empirical study, including an experiment and a questionnaire, on the impact of refactoring CPP directives with 521 experienced software developers. The results indicate that, in contrast to previous findings, comprehension performance slightly worsened in terms of correctness when our participants worked on code with refactored CPP directives. However, in alignment with previous findings, they preferred the refactored code, considering it more comprehensible and easier to work with. This dilemma of objective performance versus subjective preference is a surprising outcome that has not been found before. We argue that our work motivates the need for more studies to understand this dilemma-which may significantly impact common beliefs in research and practice.

Little L2 reading strategy research has explored the effect of linguistic and cross-cultural differences on strategic reading habits. This study attempted to fill this void by examining the reading strategies that Arabic and Mandarin speaking immigrants employed when reading and answering Canadian Language Benchmarks Assessment reading comprehension items. A chi-square analysis of their bottom-up and top-down strategy use revealed that the Mandarin-speakers used bottom-up strategies significantly more than expected, whereas the Arabicspeakers used top-down strategies more than expected. The findings of the study are discussed in the light of earlier research, and some preliminary implications for reading strategy instruction, strategy training studies, and test development practices are suggested.

Most existing studies construct a discourse structure tree following two popular methods: top-down or bottom-up strategy. However, they often suffered from cascading errors because they can not switch the strategy of building a structure tree to avoid mistakes caused by uncertain decision-making. Moreover, due to the different basis of top-down and bottom-up methods in building discourse trees, thoroughly combining the advantages of the two methods is challenging. To alleviate these issues, we propose a Bidirectional macro-level dIscourse Parser based on OracLe selEction (BIPOLE), which combines the top-down and bottom-up strategies by selecting the suitable decision-making strategy. BIPOLE consists of a basic parsing module composed of top-down and bottom-up sub-parsers and a decision-maker for selecting a prediction strategy by considering each sub-parser state. Moreover, we propose a label-based data-enhanced oracle training strategy to generate the training data of the decision-maker. Experimental results on MCDTB and RST-DT show that our model can effectively alleviate cascading errors and outperforms the SOTA baselines significantly.KeywordsMacro discourse parsingLabel embeddingBidirectional selection

Structuring processes for meat analogues

The effects of naming style and expertise on program comprehension

The paper explores the complex process of listening comprehension in Russian EFL (English as a foreign language) learners. I review the existing scholarly writing on the top-down and bottom-up strategies that listeners employ (knowingly or subconsciously) to understand the acoustic input. From the bottom-up perspective, listening comprehension is a linear process occurring in consecutive stages, each providing output that later serves as input for the next stage. In other words, listeners start by decoding phonemes and then move on to the higher levels such as words, utterances, etc., with meaning being the final step. However, empirical evidence shows that there are limitations to this rather linear approach. As recognized in Buck, listening comprehension does not occur in a fixed sequence. It seems that different types of processing may occur simultaneously. Moreover, successful decoding of the meaning does not always depend on the acoustic input itself but also on the context, communicative situation, and the listener’s general knowledge about the world. The processing that takes these factors into consideration is top-down. It relies heavily on the listener’s background knowledge. Finally, I build a case towards an interactive model of listening which seems to describe the actual comprehension process more accurately, since both top-down and bottom-up processing occur simultaneously and complement each other. I then proceed to analyze the common listening activities found in popular EFL course books and demonstrate the existing bias towards top-down listening skills development at the expense of the bottom-up processing.

Starting from the first investigations with strictly functional languages, reactive programming has been proposed as THE programming paradigm for reactive applications. The advantages of designs based on this style over designs based on the Observer design pattern have been studied for a long time. Over the years, researchers have enriched reactive languages with more powerful abstractions, embedded these abstractions into mainstream languages – including object-oriented languages – and applied reactive programming to several domains, like GUIs, animations, Web applications, robotics, and sensor networks. However, an important assumption behind this line of research – that, beside other advantages, reactive programming makes a wide class of otherwise cumbersome applications more comprehensible – has never been evaluated. In this paper, we present the design and the results of the first empirical study that evaluates the effect of reactive programming on comprehensibility compared to the traditional object-oriented style with the Observer design pattern. Results confirm the conjecture that comprehensibility is enhanced by reactive programming. In the experiment, the reactive programming group significantly outperforms the other group.

Trustworthiness includes at least two dimensions: one dimension captures the authority’s benevolence; the other captures authority’s competence. This qualitative study explores the representation of the two dimensions of authority trustworthiness: competence and benevolence. We collected free-associations about what lecturers’ competence and benevolence actually mean for Italian psychology students (n = 125). The data corpus was content-analyzed. Text units were categorized according to meaning using both a bottom-up strategy, with some categories stemming from the data (inductive reasoning), and a top-down strategy, with some categories following from the analysis of the relevant literature (deductive reasoning). Qualitative content analysis showed that these two dimensions overlapped. Students listed theoretically-defined competence characteristics as indications of both benevolence and competence. The same applied to benevolence. Overall, associations were grouped into two main dimensions: (1) the “can-do” dimension, describing a lecturer’s competence and social skill; (2) the “will-do” dimension, describing a lecturer’s good intentions, integrity, and personal motivation. In conclusion, the two conceptually distinct dimensions of trust are indistinguishable in the students’ words. These preliminary results are in line with scholars debating the multifactorial or mono-factorial nature of trust.

Two-dimensional (2-D) materials are the significantly preferred candidates for developing new optoelectronic and electronic devices due to their superlative properties and active sites which promote 2-D materials in various applications such as sensing, photodetectors, batteries, supercapacitors, thermoelectric, photocatalysis etc. 2-D materials include transition metal oxides (TMOs), transition metal dichalcogenides (TMDCs), 2-D graphene, black phosphorus, carbide, nitride-based 2-D materials. The exceptional properties of 2-D materials such as their tunable atomic thickness, larger surface-to-volume ratio, excellent electron mobility, high light absorption capacity, and their tunable bandgap in the photonic range from UV (∼390 nm) to visible light (∼480 nm) which is an extremely critical requirement in the current energy crisis scenario. The controllable synthesis of high-quality 2-D materials is the first critical step for their efficient application as their structure morphology, chemistry, thickness, surface area, and creation of defects directly controls their overall properties. This chapter summarizes two feasible approaches along with their advantages and disadvantages for the synthesis of 2-D materials, namely, the top-down strategy and bottom-up strategies. It also summarizes the specific characterization techniques to study, and understand the structural, morphological, chemical, and physical properties of the synthesized 2-D materials.

Programming style guidelines, style analyzers, and code formatters have been developed without a solid empirical or theoretical basis. In this paper we provide: (1) a justification for developing a programming style taxonomy, (2) an operational style taxonomy, (3) example applications of the taxonomy illustrating the diverse and sometimes contradictory nature of programming style guidelines, and (4) a discussion on how the taxonomy can be used to further teaching and research in programming style. The taxonomy provides a context for understanding and identifying specific style factors and empirical studies necessary to determine the effects of style on program comprehension. The results of this paper have a direct impact on programming instruction, programming standards, automated style analyzers, and code formatting tools like pretty-printers and syntax directed editors.

To support program comprehension, software artifacts can be labeled--for example within software visualization tools--with a set of representative words, hereby referred to as labels. Such labels can be obtained using various approaches, including Information Retrieval (IR) methods or other simple heuristics. They provide a bird-eye's view of the source code, allowing developers to look over software components fast and make more informed decisions on which parts of the source code they need to analyze in detail. However, few empirical studies have been conducted to verify whether the extracted labels make sense to software developers. This paper investigates (i) to what extent various IR techniques and other simple heuristics overlap with (and differ from) labeling performed by humans; (ii) what kinds of source code terms do humans use when labeling software artifacts; and (iii) what factors--in particular what characteristics of the artifacts to be labeled--influence the performance of automatic labeling techniques. We conducted two experiments in which we asked a group of students (38 in total) to label 20 classes from two Java software systems, JHotDraw and eXVantage. Then, we analyzed to what extent the words identified with an automated technique--including Vector Space Models, Latent Semantic Indexing (LSI), latent Dirichlet allocation (LDA), as well as customized heuristics extracting words from specific source code elements--overlap with those identified by humans. Results indicate that, in most cases, simpler automatic labeling techniques--based on the use of words extracted from class and method names as well as from class comments--better reflect human-based labeling. Indeed, clustering-based approaches (LSI and LDA) are more worthwhile to be used for source code artifacts having a high verbosity, as well as for artifacts requiring more effort to be manually labeled. The obtained results help to define guidelines on how to build effective automatic labeling techniques, and provide some insights on the actual usefulness of automatic labeling techniques during program comprehension tasks.