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

A Generic Approach to Fix Test Flakiness in Real-World Projects

Test flakiness, a non-deterministic behavior of builds irrelevant to code changes, is a major and continuing impediment to delivering reliable software. The very few techniques for the automated repair of test flakiness are specifically crafted to repair either Order-Dependent (OD) or Implementation-Dependent (ID) flakiness. They are also all symbolic approaches, i.e., leverage program analysis to detect and repair known test flakiness patterns and root causes, failing to generalize. To bridge the gap, we propose FlakyDoctor, a neuro-symbolic technique that combines the power of LLMs-generalizability-and program analysis-soundness-to fix different types of test flakiness. Our extensive evaluation using 873 confirmed flaky tests (332 OD and 541 ID) from 243 real-world projects demonstrates the ability of FlakyDoctor in repairing flakiness, achieving 57% (OD) and 59% (ID) success rate. Comparing to three alternative flakiness repair approaches, FlakyDoctor can repair 8% more ID tests than DexFix, 12% more OD flaky tests than ODRepair, and 17% more OD flaky tests than iFixFlakies. Regardless of underlying LLM, the non-LLM components of FlakyDoctor contribute to 12-31% of the overall performance, i.e., while part of the FlakyDoctor power is from using LLMs, they are not good enough to repair flaky tests in real-world projects alone. What makes the proposed technique superior to related research on test flakiness mitigation specifically and program repair, in general, is repairing 79 previously unfixed flaky tests in real-world projects. We opened pull requests for all cases with corresponding patches; 19 of them were accepted and merged at the time of submission

Therapeutic Possibilities of Ceftazidime Nanoparticles in Devastating Pseudomonas Ophthalmic Infections; Keratitis and Endophthalmitis

As the number of contact‐lens wearers rises worldwide, Pseudomonas aeruginosa (PA) keratitis is attracting more attention as a major public health issue. Corneal lesions of PA, being the most intimidating complication of contact‐lens wearer, can progress rapidly in spite of local antibiotic treatment, and may result in perforation and the permanent loss of vision. One of the explanations proposed for the evasion of the pathogen from immune responses of the host as well as antibacterial treatment is the fact that invasive clinical isolates of PA have the unusual ability to invade and replicate within surface corneal epithelial cells. In this manner, PA is left with an intracellular sanctuary. Endophthalmitis, albeit rare, is another ophthalmic infection faced by the challenge of drug delivery that can be potentially catastrophic. The present hypothesis is that nanoparticles can carry anti‐pseudomonas antibiotics (e.g. ceftazidime) through the membranes, into the “hidden zone” of the pathogen, hence being an effective and potent therapeutic approach against pseudomonas keratitis and endophthalmitis

Perfect is the enemy of test oracle

Automation of test oracles is one of the most challenging facets of software testing, but remains comparatively less addressed compared to automated test input generation. Test oracles rely on a ground-truth that can distinguish between the correct and buggy behavior to determine whether a test fails (detects a bug) or passes. What makes the oracle problem challenging and undecidable is the assumption that the ground-truth should know the exact expected, correct, or buggy behavior. However, we argue that one can still build an accurate oracle without knowing the exact correct or buggy behavior, but how these two might differ. This paper presents SEER, a learning-based approach that in the absence of test assertions or other types of oracle, can determine whether a unit test passes or fails on a given method under test (MUT). To build the ground-truth, SEER jointly embeds unit tests and the implementation of MUTs into a unified vector space, in such a way that the neural representation of tests are similar to that of MUTs they pass on them, but dissimilar to MUTs they fail on them. The classifier built on top of this vector representation serves as the oracle to generate "fail" labels, when test inputs detect a bug in MUT or "pass" labels, otherwise. Our extensive experiments on applying SEER to more than 5K unit tests from a diverse set of open-source Java projects show that the produced oracle is (1) effective in predicting the fail or pass labels, achieving an overall accuracy, precision, recall, and F1 measure of 93%, 86%, 94%, and 90%, (2) generalizable, predicting the labels for the unit test of projects that were not in training or validation set with negligible performance drop, and (3) efficient, detecting the existence of bugs in only 6.5 milliseconds on average.Comment: Published in ESEC/FSE 202

CodeMind: A Framework to Challenge Large Language Models for Code Reasoning

Solely relying on test passing to evaluate Large Language Models (LLMs) for code synthesis may result in unfair assessment or promoting models with data leakage. As an alternative, we introduce CodeMind, a framework designed to gauge the code reasoning abilities of LLMs. CodeMind currently supports three code reasoning tasks: Independent Execution Reasoning (IER), Dependent Execution Reasoning (DER), and Specification Reasoning (SR). The first two evaluate models to predict the execution output of an arbitrary code or code the model could correctly synthesize. The third one evaluates the extent to which LLMs implement the specified expected behavior. Our extensive evaluation of nine LLMs across five benchmarks in two different programming languages using CodeMind shows that LLMs fairly follow control flow constructs and, in general, explain how inputs evolve to output, specifically for simple programs and the ones they can correctly synthesize. However, their performance drops for code with higher complexity, non-trivial logical and arithmetic operators, non-primitive types, and API calls. Furthermore, we observe that, while correlated, specification reasoning (essential for code synthesis) does not imply execution reasoning (essential for broader programming tasks such as testing and debugging): ranking LLMs based on test passing can be different compared to code reasoning

Comparison of the corneal power measurements with the TMS4-topographer, pentacam HR, IOL master, and javal keratometer

Purpose: The aim was to compare the corneal curvature and power measured with a corneal topographer, Scheimpflug camera, optical biometer, and Javal keratometer. Materials and Methods: A total of 76 myopic individuals who were candidates for photorefractive keratectomy were selected in a cross-sectional study. Manual keratometry (Javal Schiotz type; Haag-Streit AG, Koeniz, Switzerland), automated keratometry (IOL Master version 3.02, Carl Zeiss Meditec, Jena, Germany), topography (TMS4, Tomey, Erlangen, Germany), and Pentacam HR (Oculus, Wetzlar, Germany) were performed for all participants. The 95 limits of agreement (LOAs) were reported to evaluate the agreement between devices. Results: The mean corneal power measurements were 44.3 ± 1.59, 44.25 ± 1.59, 43.68 ± 1.44, and 44.31 ± 1.61 D with a Javal keratometer, TMS4-topographer, the Pentacam and IOL Master respectively. Only the IOL Master showed no significant difference with Javal keratometer in measuring the corneal power (P = 0.965). The correlations of the Javal keratometer with TMS4-topography, Pentacam, and IOL Master was 0.991. 0.982, and 0.993 respectively. The 95 LOAs of the Javal keratometer with TMS4-topography, Pentacam, and IOL Master were - 0.361 to 0.49, -0.01 to 1.14, and - 0.36 to 0.36 D, respectively. Conclusion: Although the correlation of Pentacam, TMS4-topography, IOL Master, and Javal keratometer in measuring keratometry was high, only the IOL Master showed no significant difference with the Javal keratometer. The IOL Master had the best agreement with Javal keratometry

White-box Compiler Fuzzing Empowered by Large Language Models

Compiler correctness is crucial, as miscompilation falsifying the program behaviors can lead to serious consequences. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates tests without sufficient understanding of internal compiler behaviors. As such, they often fail to construct programs to exercise conditions of intricate optimizations. Meanwhile, traditional white-box techniques are computationally inapplicable to the giant codebase of compilers. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, prompting LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization. WhiteFox adopts a dual-model framework: (i) an analysis LLM examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) a generation LLM produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are used as feedback to further enhance the test generation on the fly. Our evaluation on four popular compilers shows that WhiteFox can generate high-quality tests to exercise deep optimizations requiring intricate conditions, practicing up to 80 more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 96 bugs, with 80 confirmed as previously unknown and 51 already fixed. Beyond compiler testing, WhiteFox can also be adapted for white-box fuzzing of other complex, real-world software systems in general

Energy Wars - Chrome vs. Firefox Which browser is more energy efficient?

This paper presents a preliminary study on the energy consump- tion of two popular web browsers. In order to properly measure the energy consumption of both environments, we simulate the usage of various applications, which the goal to mimic typical user interactions and usage. Our preliminary results show interesting findings based on ob- servation, such as what type of interactions generate high peaks of energy consumption, and which browser is overall the most efficient. Our goal with this preliminary study is to show to users how very different the efficiency of web browsers can be, and may serve with advances in this area of study.FCT -Fundação para a Ciência e a Tecnologia (UIDB/50014/2020

Therapeutic Possibilities of Ceftazidime Nanoparticles in Devastating Pseudomonas Ophthalmic Infections; Keratitis and Endophthalmitis

As the number of contactâ€lens wearers rises worldwide, Pseudomonas aeruginosa (PA) keratitis is attracting more attention as a major public health issue. Corneal lesions of PA, being the most intimidating complication of contactâ€lens wearer, can progress rapidly in spite of local antibiotic treatment, and may result in perforation and the permanent loss of vision. One of the explanations proposed for the evasion of the pathogen from immune responses of the host as well as antibacterial treatment is the fact that invasive clinical isolates of PA have the unusual ability to invade and replicate within surface corneal epithelial cells. In this manner, PA is left with an intracellular sanctuary. Endophthalmitis, albeit rare, is another ophthalmic infection faced by the challenge of drug delivery that can be potentially catastrophic. The present hypothesis is that nanoparticles can carry antiâ€pseudomonas antibiotics (e.g. ceftazidime) through the membranes, into the “hidden zone†of the pathogen, hence being an effective and potent therapeutic approach against pseudomonas keratitis and endophthalmitis