Assessing Comment Quality in Object-Oriented Languages

Previous studies have shown that high-quality code comments support developers in software maintenance and program comprehension tasks. However, the semi-structured nature of comments, several conventions to write comments, and the lack of quality assessment tools for all aspects of comments make comment evaluation and maintenance a non-trivial problem. To understand the specification of high-quality comments to build effective assessment tools, our thesis emphasizes acquiring a multi-perspective view of the comments, which can be approached by analyzing (1) the academic support for comment quality assessment, (2) developer commenting practices across languages, and (3) developer concerns about comments. Our findings regarding the academic support for assessing comment quality showed that researchers primarily focus on Java in the last decade even though the trend of using polyglot environments in software projects is increasing. Similarly, the trend of analyzing specific types of code comments (method comments, or inline comments) is increasing, but the studies rarely analyze class comments. We found 21 quality attributes that researchers consider to assess comment quality, and manual assessment is still the most commonly used technique to assess various quality attributes. Our analysis of developer commenting practices showed that developers embed a mixed level of details in class comments, ranging from high-level class overviews to low-level implementation details across programming languages. They follow style guidelines regarding what information to write in class comments but violate the structure and syntax guidelines. They primarily face problems locating relevant guidelines to write consistent and informative comments, verifying the adherence of their comments to the guidelines, and evaluating the overall state of comment quality. To help researchers and developers in building comment quality assessment tools, we contribute: (i) a systematic literature review (SLR) of ten years (2010–2020) of research on assessing comment quality, (ii) a taxonomy of quality attributes used to assess comment quality, (iii) an empirically validated taxonomy of class comment information types from three programming languages, (iv) a multi-programming-language approach to automatically identify the comment information types, (v) an empirically validated taxonomy of comment convention-related questions and recommendation from various Q&A forums, and (vi) a tool to gather discussions from multiple developer sources, such as Stack Overflow, and mailing lists. Our contributions provide various kinds of empirical evidence of the developer’s interest in reducing efforts in the software documentation process, of the limited support developers get in automatically assessing comment quality, and of the challenges they face in writing high-quality comments. This work lays the foundation for future effective comment quality assessment tools and techniques

Assessing Comment Quality in Object-Oriented Languages

Previous studies have shown that high-quality code comments support developers in software maintenance and program comprehension tasks. However, the semi-structured nature of comments, several conventions to write comments, and the lack of quality assessment tools for all aspects of comments make comment evaluation and maintenance a non-trivial problem. To understand the specification of high-quality comments to build effective assessment tools, our thesis emphasizes acquiring a multi-perspective view of the comments, which can be approached by analyzing (1) the academic support for comment quality assessment, (2) developer commenting practices across languages, and (3) developer concerns about comments. Our findings regarding the academic support for assessing comment quality showed that researchers primarily focus on Java in the last decade even though the trend of using polyglot environments in software projects is increasing. Similarly, the trend of analyzing specific types of code comments (method comments, or inline comments) is increasing, but the studies rarely analyze class comments. We found 21 quality attributes that researchers consider to assess comment quality, and manual assessment is still the most commonly used technique to assess various quality attributes. Our analysis of developer commenting practices showed that developers embed a mixed level of details in class comments, ranging from high-level class overviews to low-level implementation details across programming languages. They follow style guidelines regarding what information to write in class comments but violate the structure and syntax guidelines. They primarily face problems locating relevant guidelines to write consistent and informative comments, verifying the adherence of their comments to the guidelines, and evaluating the overall state of comment quality. To help researchers and developers in building comment quality assessment tools, we contribute: (i) a systematic literature review (SLR) of ten years (2010–2020) of research on assessing comment quality, (ii) a taxonomy of quality attributes used to assess comment quality, (iii) an empirically validated taxonomy of class comment information types from three programming languages, (iv) a multi-programming-language approach to automatically identify the comment information types, (v) an empirically validated taxonomy of comment convention-related questions and recommendation from various Q&A forums, and (vi) a tool to gather discussions from multiple developer sources, such as Stack Overflow, and mailing lists. Our contributions provide various kinds of empirical evidence of the developer’s interest in reducing efforts in the software documentation process, of the limited support developers get in automatically assessing comment quality, and of the challenges they face in writing high-quality comments. This work lays the foundation for future effective comment quality assessment tools and techniques

Interaction-aware development environments: recording, mining, and leveraging IDE interactions to analyze and support the development flow

Nowadays, software development is largely carried out using Integrated Development Environments, or IDEs. An IDE is a collection of tools and facilities to support the most diverse software engineering activities, such as writing code, debugging, and program understanding. The fact that they are integrated enables developers to find all the tools needed for the development in the same place. Each activity is composed of many basic events, such as clicking on a menu item in the IDE, opening a new user interface to browse the source code of a method, or adding a new statement in the body of a method. While working, developers generate thousands of these interactions, that we call fine-grained IDE interaction data. We believe this data is a valuable source of information that can be leveraged to enable better analyses and to offer novel support to developers. However, this data is largely neglected by modern IDEs. In this dissertation we propose the concept of "Interaction-Aware Development Environments": IDEs that collect, mine, and leverage the interactions of developers to support and simplify their workflow. We formulate our thesis as follows: Interaction-Aware Development Environments enable novel and in- depth analyses of the behavior of software developers and set the ground to provide developers with effective and actionable support for their activities inside the IDE. For example, by monitoring how developers navigate source code, the IDE could suggest the program entities that are potentially relevant for a particular task. Our research focuses on three main directions: 1. Modeling and Persisting Interaction Data. The first step to make IDEs aware of interaction data is to overcome its ephemeral nature. To do so we have to model this new source of data and to persist it, making it available for further use. 2. Interpreting Interaction Data. One of the biggest challenges of our research is making sense of the millions of interactions generated by developers. We propose several models to interpret this data, for example, by reconstructing high-level development activities from interaction histories or measure the navigation efficiency of developers. 3. Supporting Developers with Interaction Data. Novel IDEs can use the potential of interaction data to support software development. For example, they can identify the UI components that are potentially unnecessary for the future and suggest developers to close them, reducing the visual cluttering of the IDE

Prioritisation of requests, bugs and enhancements pertaining to apps for remedial actions. Towards solving the problem of which app concerns to address initially for app developers

Useful app reviews contain information related to the bugs reported by the app’s end-users along with the requests or enhancements (i.e., suggestions for improvement) pertaining to the app. App developers expend exhaustive manual efforts towards the identification of numerous useful reviews from a vast pool of reviews and converting such useful reviews into actionable knowledge by means of prioritisation. By doing so, app developers can resolve the critical bugs and simultaneously address the prominent requests or enhancements in short intervals of apps’ maintenance and evolution cycles. That said, the manual efforts towards the identification and prioritisation of useful reviews have limitations. The most common limitations are: high cognitive load required to perform manual analysis, lack of scalability associated with limited human resources to process voluminous reviews, extensive time requirements and error-proneness related to the manual efforts. While prior work from the app domain have proposed prioritisation approaches to convert reviews pertaining to an app into actionable knowledge, these studies have limitations and lack benchmarking of the prioritisation performance. Thus, the problem to prioritise numerous useful reviews still persists. In this study, initially, we conducted a systematic mapping study of the requirements prioritisation domain to explore the knowledge on prioritisation that exists and seek inspiration from the eminent empirical studies to solve the problem related to the prioritisation of numerous useful reviews. Findings of the systematic mapping study inspired us to develop automated approaches for filtering useful reviews, and then to facilitate their subsequent prioritisation. To filter useful reviews, this work developed six variants of the Multinomial Naïve Bayes method. Next, to prioritise the order in which useful reviews should be addressed, we proposed a group-based prioritisation method which initially classified the useful reviews into specific groups using an automatically generated taxonomy, and later prioritised these reviews using a multi-criteria heuristic function. Subsequently, we developed an individual prioritisation method that directly prioritised the useful reviews after filtering using the same multi-criteria heuristic function. Some of the findings of the conducted systematic mapping study not only provided the necessary inspiration towards the development of automated filtering and prioritisation approaches but also revealed crucial dimensions such as accuracy and time that could be utilised to benchmark the performance of a prioritisation method. With regards to the proposed automated filtering approach, we observed that the performance of the Multinomial Naïve Bayes variants varied based on their algorithmic structure and the nature of labelled reviews (i.e., balanced or imbalanced) that were made available for training purposes. The outcome related to the automated taxonomy generation approach for classifying useful review into specific groups showed a substantial match with the manual taxonomy generated from domain knowledge. Finally, we validated the performance of the group-based prioritisation and individual prioritisation methods, where we found that the performance of the individual prioritisation method was superior to that of the group-based prioritisation method when outcomes were assessed for the accuracy and time dimensions. In addition, we performed a full-scale evaluation of the individual prioritisation method which showed promising results. Given the outcomes, it is anticipated that our individual prioritisation method could assist app developers in filtering and prioritising numerous useful reviews to support app maintenance and evolution cycles. Beyond app reviews, the utility of our proposed prioritisation solution can be evaluated on software repositories tracking bugs and requests such as Jira, GitHub and so on

Multi-objective Search-based Mobile Testing

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis. Since crowdsourcing is, itself, an emerging research area, and less well understood than search-based software engineering, the thesis also provides the first comprehensive survey on the use of crowdsourcing in software testing (in particular) and in software engineering (more generally). This survey represents a secondary contribution. Sapienz is an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising their length, while simultaneously maximising their coverage and fault revelation. The results of empirical studies demonstrate that Sapienz significantly outperforms both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, on all three objectives. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. Octopuz reuses the Sapienz multi-objective search approach for automated JavaScript testing, aiming to investigate whether it replicates the Sapienz’ success on JavaScript testing. Experimental results on 10 real-world JavaScript apps provide evidence that Octopuz significantly outperforms the state of the art (and current state of practice) in automated JavaScript testing. Polariz is an approach that combines human (crowd) intelligence with machine (computational search) intelligence for mobile testing. It uses a platform that enables crowdsourced mobile testing from any source of app, via any terminal client, and by any crowd of workers. It generates replicable test scripts based on manual test traces produced by the crowd workforce, and automatically extracts from these test traces, motif events that can be used to improve search-based mobile testing approaches such as Sapienz

Modulation, Coding, and Receiver Design for Gigabit mmWave Communication

While wireless communication has become an ubiquitous part of our daily life and the world around us, it has not been able yet to deliver the multi-gigabit throughput required for applications like high-definition video transmission or cellular backhaul communication. The throughput limitation of current wireless systems is mainly the result of a shortage of spectrum and the problem of congestion. Recent advancements in circuit design allow the realization of analog frontends for mmWave frequencies between 30GHz and 300GHz, making abundant unused spectrum accessible. However, the transition to mmWave carrier frequencies and GHz bandwidths comes with new challenges for wireless receiver design. Large variations of the channel conditions and high symbol rates require flexible but power-efficient receiver designs. This thesis investigates receiver algorithms and architectures that enable multi-gigabit mmWave communication. Using a system-level approach, the design options between low-power time-domain and power-hungry frequency-domain signal processing are explored. The system discussion is started with an analysis of the problem of parameter synchronization in mmWave systems and its impact on system design. The proposed synchronization architecture extends known synchronization techniques to provide greater flexibility regarding the operating environments and for system efficiency optimization. For frequency-selective environments, versatile single-carrier frequency domain equalization (SC-FDE) offers not only excellent channel equalization, but also the possibility to integrate additional baseband tasks without overhead. Hence, the high initial complexity of SC-FDE needs to be put in perspective to the complexity savings in the other parts of the baseband. Furthermore, an extension to the SC-FDE architecture is proposed that allows an adaptation of the equalization complexity by switching between a cyclic-prefix mode and a reduced block length overlap-save mode based on the delay spread. Approaching the problem of complexity adaptation from time-domain, a high-speed hardware architecture for the delayed decision feedback sequence estimation (DDFSE) algorithm is presented. DDFSE uses decision feedback to reduce the complexity of the sequence estimation and allows to set the system performance between the performance of full maximum-likelihood detection and pure decision feedback equalization. An implementation of the DDFSE architecture is demonstrated as part of an all-digital IEEE802.11ad baseband ASIC manufactured in 40nm CMOS. A flexible architecture for wideband mmWave receivers based on complex sub-sampling is presented. Complex sub-sampling combines the design advantages of sub-sampling receivers with the flexibility of direct-conversion receivers using a single passive component and a digital compensation scheme. Feasibility of the architecture is proven with a 16Gb/s hardware demonstrator. The demonstrator is used to explore the potential gain of non-equidistant constellations for high-throughput mmWave links. Specifically crafted amplitude phase-shift keying (APSK) modulation achieve 1dB average mutual information (AMI) advantage over quadrature amplitude modulation (QAM) in simulation and on the testbed hardware. The AMI advantage of APSK can be leveraged for a practical transmission using Polar codes which are trained specifically for the constellation

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

Determinants of Success of the Open Source Selective Revealing Strategy: Solution Knowledge Emergence

Recent research suggests that firms may be able to create a competitive advantage by deliberately revealing specific problem knowledge beyond firm boundaries to open source meta-organisations such that new solution knowledge is created that benefits the focal firm more than its competitors (Alexy, George, & Salter, 2013). Yet, not all firms that use knowledge revealing strategies are successful in inducing the emergence of solution knowledge. The extant literature has as of yet not explained this heterogeneity in success of knowledge revealing strategies. Using a longitudinal database spanning the period from 1998 to end 2012 with more than 2 billion data points that was obtained from the Mozilla Foundation, one of the top open source meta-organisations, this dissertation identifies and measures the antecedent factors affecting successful solution knowledge emergence. The results reveal 35 antecedent factors that affect solution knowledge emergence in different ways across three levels of analysis. The numerous contributions to theory and practice that follow from the results are discussed