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Register a new userNavigate, Understand, Communicate: How Developers Locate Performance Bugs
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Added by
Sebastian Baltes
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Background: Performance bugs can lead to severe issues regarding computation efficiency, power consumption, and user experience. Locating these bugs is a difficult task because developers have to judge for every costly operation whether runtime is consumed necessarily or unnecessarily. Objective: We wanted to investigate how developers, when locating performance bugs, navigate through the code, understand the program, and communicate the detected issues. Method: We performed a qualitative user study observing twelve developers trying to fix documented performance bugs in two open source projects. The developers worked with a profiling and analysis tool that visually depicts runtime information in a list representation and embedded into the source code view. Results: We identified typical navigation strategies developers used for pinpointing the bug, for instance, following method calls based on runtime consumption. The integration of visualization and code helped developers to understand the bug. Sketches visualizing data structures and algorithms turned out to be valuable for externalizing and communicating the comprehension process for complex bugs. Conclusion: Fixing a performance bug is a code comprehension and navigation problem. Flexible navigation features based on executed methods and a close integration of source code and performance information support the process.
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Context. As a novel coronavirus swept the world in early 2020, thousands of software developers began working from home. Many did so on short notice, under difficult and stressful conditions. Objective. This study investigates the effects of the pandemic on developers wellbeing and productivity. Method. A questionnaire survey was created mainly from existing, validated scales and translated into 12 languages. The data was analyzed using non-parametric inferential statistics and structural equation modeling. Results. The questionnaire received 2225 usable responses from 53 countries. Factor analysis supported the validity of the scales and the structural model achieved a good fit (CFI = 0.961, RMSEA = 0.051, SRMR = 0.067). Confirmatory results include: (1) the pandemic has had a negative effect on developers wellbeing and productivity; (2) productivity and wellbeing are closely related; (3) disaster preparedness, fear related to the pandemic and home office ergonomics all affect wellbeing or productivity. Exploratory analysis suggests that: (1) women, parents and people with disabilities may be disproportionately affected; (2) different people need different kinds of support. Conclusions. To improve employee productivity, software companies should focus on maximizing employee wellbeing and improving the ergonomics of employees home offices. Women, parents and disabled persons may require extra support.
Following the onset of the COVID-19 pandemic and subsequent lockdowns, software engineers daily life was disrupted and they were abruptly forced into working remotely from home. Across one exploratory and one confirmatory study (N = 482), we tested whether a typical working day is different to pre-pandemic times and whether specific tasks are associated with task-specific satisfaction and productivity. To explore the subject domain, we first run a two-wave longitudinal study, where we found that the time software engineers spent doing specific tasks (e.g., coding, bugfixing, helping others) from home was similar to pre-pandemic times. Also, the amount of time developers spent on each task was unrelated to their general well-being, perceived productivity, and other variables such as basic needs. In our confirmatory study, we found that task satisfaction and productivity are predicted by task-specific variables (e.g., how much autonomy software engineers had during coding) but not by task-independent variables such as general resilience or a good work-life balance. Additionally, we found that satisfaction and autonomy were significantly higher when software engineers were helping others and lower when they were bugfixing. Also, contrary to anecdotal evidence, software engineers satisfaction and productivity during meetings is not lower compared to other tasks. Finally, we discuss implications for software engineers, management, and researchers.
Software developers are turning to vlogs (video blogs) to share what a day is like to walk in their shoes. Through these vlogs developers share a rich perspective of their technical work as well their personal lives. However, does the type of activities portrayed in vlogs differ from activities developers in the industry perform? Would developers at a software company prefer to show activities to different extents if they were asked to share about their day through vlogs? To answer these questions, we analyzed 130 vlogs by software developers on YouTube and conducted a survey with 335 software developers at a large software company. We found that although vlogs present traditional development activities such as coding and code peripheral activities (11%), they also prominently feature wellness and lifestyle related activities (47.3%) that have not been reflected in previous software engineering literature. We also found that developers at the software company were inclined to share more non-coding tasks (e.g., personal projects, time spent with family and friends, and health) when asked to create a mock-up vlog to promote diversity. These findings demonstrate a shift in our understanding of how software developers are spending their time and find valuable to share publicly. We discuss how vlogs provide a more complete perspective of software development work and serve as a valuable source of data for empirical research.
Static bug finders have been widely-adopted by developers to find bugs in real world software projects. They leverage predefined heuristic static analysis rules to scan source code or binary code of a software project, and report violations to these rules as warnings to be verified. However, the advantages of static bug finders are overshadowed by such issues as uncovered obvious bugs, false positives, etc. To improve these tools, many techniques have been proposed to filter out false positives reported or design new static analysis rules. Nevertheless, the under-performance of bug finders can also be caused by the incorrectness of current rules contained in the static bug finders, which is not explored yet. In this work, we propose a differential testing approach to detect bugs in the rules of four widely-used static bug finders, i.e., SonarQube, PMD, SpotBugs, and ErrorProne, and conduct a qualitative study about the bugs found. To retrieve paired rules across static bug finders for differential testing, we design a heuristic-based rule mapping method which combines the similarity in rules description and the overlap in warning information reported by the tools. The experiment on 2,728 open source projects reveals 46 bugs in the static bug finders, among which 24 are fixed or confirmed and the left are awaiting confirmation. We also summarize 13 bug patterns in the static analysis rules based on their context and root causes, which can serve as the checklist for designing and implementing other rules and or in other tools. This study indicates that the commonly-used static bug finders are not as reliable as they might have been envisaged. It not only demonstrates the effectiveness of our approach, but also highlights the need to continue improving the reliability of the static bug finders.
Resource leak bugs in Android apps are pervasive and can cause serious performance degradation and system crashes. In recent years, several resource leak detection techniques have been proposed to assist Android developers in correctly managing system resources. Yet, there exist no common bug benchmarks for effectively and reliably comparing such techniques and quantitatively evaluating their strengths and weaknesses. This paper describes our initial contribution towards constructing such a benchmark. To locate real resource leak bugs, we mined 124,215 code revisions of 34 large-scale open-source Android apps. We successfully found 298 fixed resource leaks, which cover a diverse set of resource classes, from 32 out of the 34 apps. To understand the characteristics of these bugs, we conducted an empirical study, which revealed the root causes of frequent resource leaks in Android apps and common patterns of faults made by developers. With our findings, we further implemented a static checker to detect a common pattern of resource leaks in Android apps. Experiments showed that the checker can effectively locate real resource leaks in popular Android apps, confirming the usefulness of our work.
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