Real-world Programs Research Articles

PCR187 Patient and Physician Satisfaction of Treatment with Eculizumab or Ravulizumab in Paroxysmal Nocturnal Hemoglobinuria: Results from a Real-World Disease-Specific Programme

Using patient-level data from an Adelphi Disease Specific Programme (DSP), we describe physician-reported and self-reported patient satisfaction with eculizumab or ravulizumab for the treatment of paroxysmal nocturnal hemoglobinuria (PNH).

IFDS-based Context Debloating for Object-Sensitive Pointer Analysis

Object-sensitive pointer analysis, which separates the calling contexts of a method by its receiver objects, is known to achieve highly useful precision for object-oriented languages such as Java. Despite recent advances, all object-sensitive pointer analysis algorithms still suffer from the scalability problem due to the combinatorial explosion of contexts in large programs. In this article, we introduce a new approach, Conch , that can be applied to debloat contexts for all object-sensitive pointer analysis algorithms, thereby improving significantly their efficiency while incurring a negligible loss of precision. Our key insight is to approximate a recently proposed set of two necessary conditions for an object in a program to be context-sensitive, i.e., context-dependent (whose precise verification is undecidable) with a set of three linearly verifiable conditions in terms of the number of edges in the pointer assignment graph (PAG) representation of the program. These three linearly verifiable conditions, which turn out to be almost always necessary in practice, are synthesized from three key observations regarding context-dependability for the objects created and used in real-world object-oriented programs. To develop a practical implementation for Conch , we introduce an IFDS-based algorithm for reasoning about object reachability in the PAG of a program, which runs linearly in terms of the number of edges in the PAG. By debloating contexts for three representative object-sensitive pointer analysis algorithms, which are applied to a set of representative Java programs, Conch can speed up these three baseline algorithms substantially at only a negligible loss of precision (less than 0.1%) with respect to several commonly used precision metrics. In addition, Conch also improves their scalability by enabling them to analyze substantially more programs to completion than before (under a time budget of 12 hours). Conch has been open-sourced (http://www.cse.unsw.edu.au/~corg/tools/conch), opening up new opportunities for other researchers and practitioners to further improve this research. To demonstrate this, we introduce one extension of Conch to accelerate further the three baselines without losing any precision, providing further insights on extending Conch to make precision-efficiency tradeoffs in future research.

Testing Feedforward Neural Networks Training Programs

At present, we are witnessing an increasing effort to improve the performance and trustworthiness of Deep Neural Networks (DNNs), with the aim to enable their adoption in safety critical systems such as self-driving cars or aircraft collision-avoidance systems. Multiple testing techniques are proposed to generate test cases that can expose inconsistencies in the behavior of DNN models. These techniques assume implicitly that the training program is bug-free and appropriately configured. However, satisfying this assumption for a novel problem requires significant engineering work to prepare the data, design the DNN, implement the training program, and tune the hyperparameters to produce the model for which current automated test data generators search for corner-case behaviors. All these model training steps can be error prone. Therefore, it is crucial to detect and correct errors throughout all the engineering steps of DNN-based software systems and not only on the resulting DNN model. In this article, we gather a catalog of training issues and based on their symptoms and their effects on the behavior of the training program, we propose practical verification routines to detect the aforementioned issues, automatically, by continuously validating that some important properties of the learning dynamics hold during the training. Then, we design TheDeepChecker , an end-to-end property-based debugging approach for DNN training programs and implement it as a TensorFlow-based library. As an empirical evaluation, we conduct a case study to assess the effectiveness of TheDeepChecker on synthetic and real-world buggy DL programs and compare its performance to that of the Amazon SageMaker Debugger ( SMD ). Results show that TheDeepChecker ’s on-execution validation of DNN-based program’s properties through three sequential phases (pre-, on-, and post-fitting) succeeds in revealing several coding bugs and system misconfigurations errors early on and at a low cost. Moreover, our property-based approach outperforms the SMD ’s offline rules verification on training logs in terms of detection accuracy for unstable learning issues and coverage of additional DL bugs.

A unit-based symbolic execution method for detecting memory corruption vulnerabilities in executable codes

Memory corruption is a serious class of software vulnerabilities, which requires careful attention to be detected and removed from applications before getting exploited and harming the system users. Symbolic execution is a well-known method for analyzing programs and detecting various vulnerabilities, e.g., memory corruption. Although this method is sound and complete in theory, it faces some challenges, such as path explosion, when applied to real-world complex programs. In this paper, we present a method for improving the efficiency of symbolic execution and detecting four classes of memory corruption vulnerabilities in executable codes, i.e., heap-based buffer overflow, stack-based buffer overflow, use-after-free, and double-free. We perform symbolic execution only on test units rather than the whole program to lower the chance of path explosion. In our method, test units are considered parts of the program’s code, which might contain vulnerable statements and are statically identified based on the specifications of memory corruption vulnerabilities. Then, each test unit is symbolically executed to calculate path and vulnerability constraints for each statement of the unit, which determine the conditions on unit input data for executing that statement or activating vulnerabilities in it, respectively. Solving these constraints gives us input values for the test unit, which execute the desired statements and reveal vulnerabilities in them. Finally, we use machine learning to approximate the correlation between system and unit input data. Thereby, we generate system inputs that enter the program, reach vulnerable instructions in the desired test unit, and reveal vulnerabilities in them. This method is implemented as a plug-in for angr framework and evaluated using a group of benchmark programs. The experiments show its superiority over similar tools in accuracy and performance.

Relaxed Effective Callback Freedom: A Parametric Correctness Condition for Sequential Modules With Callbacks

Callbacks are an essential mechanism for event-driven programming. Unfortunately, callbacks make reasoning challenging because they introduce behaviors where calls to the module are interleaved. We present a parametric method that, from a particular invariant of the program, allows reducing the problem of verifying the invariant in the presence of callbacks, to the callback-free setting. Intuitively, we allow callbacks to introduce behaviors that cannot be produced by callback free executions, as long as they do not affect correctness. A chief insight is that the user is aware of the potential effect of the callbacks on the program state. To this end, we present a parametric verification technique which accepts this insight as a relation between callback and callback free executions. We implemented our approach and applied it successfully to a large set of real-world programs.

RLTG: Multi-targets directed greybox fuzzing.

Directed greybox fuzzing guides fuzzers to explore specific objective code areas and has achieved good performance in some scenarios such as patch testing. However, if there are multiple objective code to explore, existing directed greybox fuzzers, such as AFLGo and Hawkeye, often neglect some targets because they use harmonic means of distance and prefers to test those targets with shorter reachable path. Besides, existing directed greybox fuzzers cannot calculate the accurate distance due to indirect calls in the program. In addition, existing directed greybox fuzzers fail to address the exploration and exploitation problem and have poor efficiency in seed scheduling. To address these problems, we propose a dynamic seed distance calculation scheme, it increase the seed distance dynamically when the reachable path encounter indirect call. Besides, the seed distance calculation can deal with the bias problem in multi-targets scenarios. With the seed distance calculation method, we propose a new seed scheduling algorithm based on the upper confidence bound algorithm to deal with the exploration and exploitation problem in drected greybox fuzzing. We implemented a prototype RLTG and evaluate it on real-world programs. Evaluation of our prototype shows that our approach outperforms a state-of-the-art directed fuzzer AFLGo. On the multi-targets benchmark Magma, RLTG reproduces bugs with 6.9x speedup and finds 66.7% more bugs than AFLGo.

Weight loss maintenance after a digital commercial behaviorchange program (Noom Weight): Observational cross-sectional survey study.

Behavioral weight loss programs often lead to significant short-term weight loss, but long-term weight maintenance remains a challenge. Most weight maintenance data come from clinical trials, in-person programs, or general population surveys, but there is a need for better understanding of long-term weight maintenance in real-world digital programs. This observational survey study examined weight maintenance reported by individuals who had used Noom Weight, a digital commercial behavior change program, and identified factors associated with greater weight maintenance. The cross-sectional survey was completed by 840 individuals who had lost at least 10% of their body weight using Noom Weight 6-24months prior. The study found that 75% of individuals maintained at least 5% weight loss after 1year, and 49% maintained 10% weight loss. On average, 65% of initial weight loss was maintained after 1year and 57% after 2years. Habitual behaviors, such as healthy snacking and exercise, were associated with greater weight maintenance, while demographic factors were not. This study provides real-world data on the long-term weight maintenance achieved using a fully digital behavioral program. The results suggest that Noom Weight is associated with successful weight maintenance in a substantial proportion of users. Future research will use a randomized controlled trial to track weight maintenance after random assignment and at a 2year follow-up.

UltraFuzz: Towards Resource-Saving in Distributed Fuzzing

Recent research has sought to improve fuzzing performance via parallel computing. However, researchers focus on improving efficiency while ignoring the increasing cost of testing resources. Parallel fuzzing in the distributed environment amplifies the resource-wasting problem caused by the random nature of fuzzing. In the parallel mode, owing to the lack of an appropriate task dispatching scheme and timely fuzzing status synchronization among different fuzzing instances, task conflicts and workload imbalance occur, making the resource-wasting problem severe. In this paper, we design UltraFuzz, a fuzzer for resource-saving in distributed fuzzing. Based on centralized dynamic scheduling, UltraFuzz can dispatch tasks and schedule power globally and reasonably to avoid resource-wasting. Besides, UltraFuzz can elastically allocate computing power for fuzzing and seed evaluation, thereby avoiding the potential bottleneck of seed evaluation that blocks the fuzzing process. UltraFuzz was evaluated using real-world programs, and the results show that with the same testing resource, UltraFuzz outperforms state-of-the-art tools, such as AFL, AFL-P, PAFL, and EnFuzz. Most importantly, the experiment reveals certain results that seem counter-intuitive, namely that parallel fuzzing can achieve ``super-linear acceleration'' when compared with single-core fuzzing. We conduct additional experiments to reveal the deep reasons behind this phenomenon and dig deep into the inherent advantages of parallel fuzzing over serial fuzzing, including the global optimization of seed energy scheduling and the escape of local optimal seed. Additionally, 24 real-world vulnerabilities were discovered using UltraFuzz.

162 - The impact of real-world lung cancer screening programmes on downstream diagnostic capacity

162 - The impact of real-world lung cancer screening programmes on downstream diagnostic capacity

Fuzzing Configurations of Program Options

While many real-world programs are shipped with configurations to enable/disable functionalities, fuzzers have mostly been applied to test single configurations of these programs. In this work, we first conduct an empirical study to understand how program configurations affect fuzzing performance. We find that limiting a campaign to a single configuration can result in failing to cover a significant amount of code. We also observe that different program configurations contribute differing amounts of code coverage, challenging the idea that each one can be efficiently fuzzed individually. Motivated by these two observations, we propose ConfigFuzz , which can fuzz configurations along with normal inputs. ConfigFuzz transforms the target program to encode its program options within part of the fuzzable input, so existing fuzzers’ mutation operators can be reused to fuzz program configurations. We instantiate ConfigFuzz on six configurable, common fuzzing targets, and integrate their executions in FuzzBench. In our evaluation, ConfigFuzz outperforms two baseline fuzzers in four targets, while the results are mixed in the other targets due to program size and configuration space. We also analyze the options fuzzed by ConfigFuzz and how they affect the performance.

An annotation-based approach for finding bugs in neural network programs

As neural networks are increasingly included as core components of safety–critical systems, developing effective testing techniques specialized for them becomes crucial. The bulk of the research has focused on testing neural-network models; but these models are defined by writing programs, and there is growing evidence that these neural-network programs often have bugs too.This paper presents aNNoTest: an approach to generating test inputs for neural-network programs. A fundamental challenge is that the dynamically-typed languages (e.g., Python) commonly used to program neural networks cannot express detailed constraints about valid function inputs (e.g., matrices with certain dimensions). Without knowing these constraints, automated test-case generation is prone to producing invalid inputs, which trigger spurious failures and are useless for identifying real bugs. To address this problem, we introduce a simple annotation language tailored for concisely expressing valid function inputs in neural-network programs. aNNoTest takes as input an annotated program, and uses property-based testing to generate random inputs that satisfy the validity constraints. In the paper, we also outline guidelines that simplify writing aNNoTest annotations.We evaluated aNNoTest on 19 neural-network programs from Islam et al’s survey. Islam et al. (2019), which we manually annotated following our guidelines — producing 6 annotations per tested function on average. aNNoTest automatically generated test inputs that revealed 94 bugs, including 63 bugs that the survey reported for these projects. These results suggest that aNNoTest can be a valuable approach to finding widespread bugs in real-world neural-network programs.

Combining rule- and SMT-based reasoning for verifying floating-point Java programs in KeY

Deductive verification has been successful in verifying interesting properties of real-world programs. One notable gap is the limited support for floating-point reasoning. This is unfortunate, as floating-point arithmetic is particularly unintuitive to reason about due to rounding as well as the presence of the special values infinity and ‘Not a Number’ (NaN). In this article, we present the first floating-point support in a deductive verification tool for the Java programming language. Our support in the KeY verifier handles floating-point arithmetics, transcendental functions, and potentially rounding-type casts. We achieve this with a combination of delegation to external SMT solvers on the one hand, and KeY-internal, rule-based reasoning on the other hand, exploiting the complementary strengths of both worlds. We evaluate this integration on new benchmarks and show that this approach is powerful enough to prove the absence of floating-point special values—often a prerequisite for correct programs—as well as functional properties, for realistic benchmarks.

Are Real-World Prosociality Programs Associated with Greater Psychological Well-Being in Primary School-Aged Children?

Quality education can build a sustainable, happier world, but what experiences support student well-being? Numerous laboratory studies suggest that prosocial behavior predicts greater psychological well-being. However, relatively little work has examined whether real-world prosociality programs are associated with greater well-being in primary school-aged children (aged 5-12). In Study 1, we surveyed 24/25 students who completed their 6th Grade curriculum in a long-term care home alongside residents called "Elders," which offered numerous opportunities for planned and spontaneous helping. We found that the meaning that students derived from their prosocial interactions with the Elders was strongly associated with greater psychological well-being. In Study 2, we conducted a pre-registered field experiment with 238 primary school-aged children randomly assigned to package essential items for children who experience homelessness and/or poverty who were either demographically similar or dissimilar in age and/or gender to them as part of a classroom outing. Children self-reported their happiness both pre- and post-intervention. While happiness increased from pre- to post-intervention, this change did not differ for children who helped a similar or dissimilar recipient. These studies offer real-world evidence consistent with the possibility that engaging in prosocial classroom activities-over an afternoon or year-is associated with greater psychological well-being in primary school-aged children.

TAnnotator: Towards Annotating Programming E-textbooks with Facts and Examples

E-textbooks are one of the commonly used sources to learn programming, in the domain of computer science and engineering. Programming related textbooks provide examples related to syntax, but the number of examples are often limited. Thus, beginners who use e-textbooks often visit other sources on the internet for examples and other information. Adding dynamic information to programming related e-textbooks such as additional information about topics of discussion and real-world programming examples could enhance readers’ experience, and improve their learning. Hence, towards enhancing user experience with programming-based e-textbooks, we present TAnnotator, a web-based portal that dynamically annotates computer-programming based e-textbook, The C++ Tutorial, with related programming examples and tooltips. The tooltips aim to provide further knowledge to the readers about various concepts being discussed in textbooks by providing related facts adjacent to the text of the topic in the e-textbook. TAnnotator has been evaluated to assess the usefulness, user experience and complexity using UTAUT2 model through a user survey with 15 volunteers. The results of the survey indicated that TAnnotator was useful in providing additional knowledge on top of the e-textbook.

The 5R Guidelines for a strengths-based approach to co-design with customers experiencing vulnerability

This research note addresses a significant gap in the literature in researching marginalised customers who may potentially experience vulnerability: the need to use a strengths-based approach in designing social marketing research. There has been a (positive) trend in recent decades towards greater inclusion of stakeholders in social marketing research, design and evaluation through the adoption of co-design methods. However, a theoretical issue that has not been adequately addressed within social marketing research (with some exceptions) is that it is possible to use co-design methods in such a way that the language and approaches that are employed serve to further disempower these groups through a deficit-based discourse. This research note uses reflexivity to propose a set of guidelines for how to implement a strengths-based approach when co-designing with customers experiencing vulnerability, specifically from a social marketing perspective. A real-world program in the context of empowering mature women to maintain secure housing is used to illustrate the guidelines.

Start small and let it build; a mixed-method evaluation of a school-based physical activity program, Kilometre Club

BackgroundDespite the benefits of physical activity, there is minimal research focusing on factors that influence real-world school-based physical activity programs. Kilometre (KM) Club is an Australian grassroots program which aims to increase physical activity in students through the completion of an outside walk or run at school. This small-scale pilot evaluation aimed to examine families, teachers and principals’ perceptions of the benefits, enablers and barriers of KM Club. It also aimed to examine the effects of KM Club on student’s physical activity levels during the school day.MethodsFour regional New South Wales (NSW) primary schools participated in this study. 26 families, four teachers, and two principals from School A, C, B and D completed semi-structured interviews to understand their perceptions of KM Club. 21 students completed emotional state-scales to understand their emotions when participating in KM Club. 141 students from Schools B, C and D participated in step-count measures using accelerometers.ResultsFamilies, teachers and principals reported a range of benefits such as improved social connectedness, wellbeing, home and classroom behaviours, participation in sport and fitness levels. Enablers consisted of champion engagement, incentives, versatile facilities and integration with other school activities. Identified barriers included the weather and environment, program timing and health issues. Most students reported that participating in KM Club made them feel proud, confident and fantastic. School B reported a significant increase in students' daily step counts on KM Club days compared to non-KM Club days (+ 15%; p = 0.001), while School C reported no significant changes (-5%; p = 0.26). School D reported a significant increase in the number of daily steps taken by KM Club participants compared with non-KM club participants (+ 10%; p = 0.024).ConclusionThere is no one-size-fits-all approach to implementing school-based physical activity initiatives. However, it appears that flexible and adaptable factors are important to the successful implementation of school-based programs, such as KM Club. This study revealed a variety of self-reported health, wellbeing and educational benefits for students, as well as an increase in student’s physical activity levels at 2 of the 3 schools participating in the quantitative data collection. This pilot evaluation may help to inform future design, implementation and scale-up of KM Club and school-based health promotion programs, potentially improving child health, wellbeing and educational outcomes.Trial registration(LNR223 – LNR/19/NCC/45).

A Study on the Effects of Immersive Content Use on Positive affect, Self-esteem, and Psychological Happiness of Disabled Persons: To advocate the dissemination of immersive content facilities in island areas

The purpose of this study is to analyze the structural relationships among immersive content use satisfaction, positive affect, self-esteem, and psychological happiness of persons with disabilities and thereby investigate the positive effect of immersive content experience on disabled persons who wish to have immersive content facilities for disabled persons in island areas. Using survey data collected from physically and mentally disabled persons using immersive content facilities, the study found that disabled persons’ satisfaction with immersive content was significantly and positively associated with their positive affect, self-esteem, and psychological happiness; positive affect and self-esteem of disabled persons using immersive content were significantly and positively associated with their psychological happiness; positive affect and self-esteem significantly mediated the relationship between use satisfaction of disabled persons using immersive content and their psychological happiness. This study is meaningful in that it is the first study that empirically investigated the effect of immersive content facilities on disabled persons and shed light on the potential role of immersive content in improving the quality of life and happiness of disabled persons by enabling them to virtually experience real-world programs.

The dynamics of poverty targeting

Several recent papers have compared the ability of proxy-means testing (PMT) and community-based targeting (CBT) to identify consumption-poor households. Motivated by the facts that targeted benefits typically reach beneficiaries with a substantial time lag and that transitions into and out of poverty are frequent, we assess PMT’s and CBT’s performance during the years following the targeting exercise. With original data from Burkina Faso, we replicate the finding that PMT outperforms CBT at baseline. But this pattern disappears for households’ poverty status 12 and 30 months later where both methods perform equally well. For real-world welfare programs where benefits typically accrue with a substantial time lag, our results suggest that CBT’s targeting performance is better than commonly thought.

Predicting Integer Overflow Errors via Supervised Learning

An integer overflow error occurs when an integer operation in computer software evaluates a value out of the integer range. It can lead to a fatal system failure. The existing approaches to detecting integer overflow errors rely on data/control-flow analysis of the code or execution of the code with test cases. This paper presents a supervised learning approach to predicting whether each method in a given Java program has an integer overflow error by treating the source code as text. Built upon real-world programs, our Java dataset covers all integer data types and operations in Java, the methods for preventing integer overflow errors, and adversarial samples. We have evaluated six classification models, BERT, DistilBERT, codeBERT, Code2Vec, fastText, and NBSVM. They represent different text embedding techniques for dealing with source code. The experiment results show that BERT and its variants have outperformed other models. We have applied the resultant BERT model to eleven real-world projects, including JDK13.0 and ten top-ranked GitHub projects, and revealed 181 integer overflow errors. In addition, we have evaluated the classification models with a C/C++ dataset. The result is similar to that of the Java dataset.

RWD48 Introduction to Odyssey: Real-World Rare Disease Data Collection Program From Digitized Health Records of Patients With Glycogen Storage Disease Type III (GSDIII) in the United States

RWD48 Introduction to Odyssey: Real-World Rare Disease Data Collection Program From Digitized Health Records of Patients With Glycogen Storage Disease Type III (GSDIII) in the United States