Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

Підхід з використанням нечіткої логіки до автоматизованої ідентифікації дефектів в розподілених програмних системах та лінійках програмних продуктів

An approach to the improvement of the efficiency of the bug tracking process in distributed software systems and software product lines via automated identification of duplicate report groups and report groups collected from correlated bugs, combined with bug localization within a software product line is considered. A brief overview of the problem of automated report collection and aggregation is made, several existing software tools and solutions for report management and analysis are reviewed, and basic functionality of a typical report management system is identified. In addition to this, a concept of a report correlation group is introduced and an automated crash report aggregation method based on the rules for comparison of crash signatures, top frames, and frequent closed ordered sub-sets of frames of crash reports is proposed. To evaluate these rules, two separate fuzzy models are built, the first one to calculate the output of the Frequent Closed Ordered Sub-Set Comparison rule, and the second one to interpret and combine the output of all three rules and produce an integrated degree of crash report’s similarity to an existing report correlation group or to another report. A prototype of a report management system with report aggregation capabilities is developed and tested using imported from the publicly available Mozilla Crash Stats project report groups. During the experiment, a precision of 90% and a recall of 81% are achieved. Lastly, an approach to localize the largest identified report groups and represented by them bugs within a concrete software product line based on an information basis consisting of a feature model, a list of software components, and a mapping between features and components is proposed, conclusions are drawn, and goals for the future work are outlined.Розглянуто підхід до підвищення ефективності процесу відстеження помилок в розподілених програмних системах та лінійках програмних продуктів шляхом автоматизованої ідентифікації дубльованих груп звітів та груп звітів, зібраних з корельованих помилок, у поєднанні з локалізацією помилок серед компонентів лінійок програмних продуктів. Зроблено короткий огляд проблеми автоматизованого збору та агрегації звітів, розглянуто кілька існуючих програмних засобів для аналізу звітів, а також визначено основні функціональні можливості типової системи управління звітами. Крім того, запропоновано концепцію кореляційної групи звітів та наведено автоматизований метод агрегації звітів, який базується на правилах порівняння підписів звітів, верхньої форми звітів, та трасувальних стеків звітів про збої. Для оцінки цих правил будуються дві окремі нечіткі моделі – перша для розрахунку результату правила порівняння трасувальних стеків звітів, а друга - для інтерпретації та поєднання результатів усіх трьох правил і створення інтегрованого ступеня подібності звіту про збій з існуючою кореляційною групою звітів або іншим звітом. За допомогою імпорту груп звітів з загальнодоступного репозиторію Mozilla, тестується розроблений прототип системи управління та агрегації звітів. Під час експерименту досягається точність в 90% і повнота в 81%. Нарешті, пропонується підхід до локалізації найбільших ідентифікованих груп звітів та представлених ними помилок у лінійці програмних продуктів на основі інформаційної бази, що складається з функціональної моделі, списку програмних компонентів та взаємозв’язків між функціями та компонентами, робляться висновки та визначаються цілі для подальшої роботи

Duplicate Defect Detection

Discovering and fixing faults is an unavoidable process in Software Engineering. It is always a good practice to document and organize fault reports. This facilitates the effectiveness of development and maintenance process. Bug Tracking Repositories, such as Bugzilla, are designed to provide fault reporting facilities for developers, testers and users of the system. Allowing anyone to contribute finding and reporting faults has an immediate impact on software quality. However, this benefit comes with one side-effect. Users often file reports that describe the same fault. This increases the triaging time spent by the maintainers. At the same time, important information required to fix the fault is likely to be distributed across different reports.;The objective of this thesis is twofold. First, we want to understand the dynamics of bug report filing for a large, long duration open source project, Firefox. Second, we present a new approach that can reduce the number of duplicate reports. The novel element in the proposed approach is the ability to concentrate the search for duplicates on specific portions of the bug repository. This improves the performance of Information Retrieval techniques and classification runtime of our algorithm. Our system can be deployed as a search tool to help reporters query the repository or it can be adopted to help maintainers detect duplicate reports. In both cases the performance is satisfactory. When tested as a search tool our system is able to detect up to 53% of duplicate reports. The approach adapted for maintainers has a maximum recall rate of 59%

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4

Big Data: Learning, Analytics, and Applications

With the rise of autonomous systems, the automation of faults detection and localization becomes critical to their reliability. An automated strategy that can provide a ranked list of faulty modules or files with respect to how likely they contain the root cause of the problem would help in the automation bug localization. Learning from the history if previously located bugs in general, and extracting the dependencies between these bugs in particular, helps in building models to accurately localize any potentially detected bugs. In this study, we propose a novel fault localization solution based on a learning-to-rank strategy, using the history of previously localized bugs and their dependencies as features, to rank files in terms of their likelihood of being a root cause of a bug. The evaluation of our approach has shown its efficiency in localizing dependent bugs

Fixing Bug Reporting for Mobile and GUI-Based Applications

Smartphones and tablets have established themselves as mainstays in the modern computing landscape. It is conceivable that in the near future such devices may supplant laptops and desktops, becoming many users primary means of carrying out typical computer assisted tasks. In turn, this means that mobile applications will continue on a trajectory to becoming more complex, and the primary focus of millions of developers worldwide. In order to properly create and maintain these apps developers will need support, especially with regard to the prompt confirmation and resolution of bug reports. Unfortunately, current issue tracking systems typically only implement collection of coarse grained natural language descriptions, and lack features to facilitate reporters including important information in their reports. This illustrates the lexical information gap that exists in current bug reporting systems for mobile and GUI-based apps. This paper outlines promising preliminary work towards addressing this problem and proposes a comprehensive research program which aims to implement new bug reporting mechanisms and examine the impact that they might have on related software maintenance tasks