@article{qiu_shao_zhao_khan_hui_jin_2022, title={A Deep Study of the Effects and Fixes of Server-Side Request Races in Web Applications}, ISSN={["2160-1852"]}, DOI={10.1145/3524842.3528463}, abstractNote={Server-side web applications are vulnerable to request races. While some previous studies of real-world request races exist, they primarily focus on the root cause of these bugs. To better combat request races in server-side web applications, we need a deep understanding of their characteristics. In this paper, we provide a complementary focus on race effects and fixes with an enlarged set of request races from web applications developed with Object-Relational Mapping (ORM) frameworks. We revisit characterization questions used in previous studies on newly included request races, distinguish the external and internal effects of request races, and relate requestrace fixes with concurrency control mechanisms in languages and frameworks for developing server-side web applications. Our study reveals that: (1) request races from ORM-based web applications share the same characteristics as those from raw-SQL web applications; (2) request races violating application semantics without explicit crashes and error messages externally are common, and latent request races, which only corrupt some shared resource internally but require extra requests to expose the misbehavior, are also common; and (3) various fix strategies other than using synchronization mechanisms are used to fix request races. We expect that our results can help developers better understand request races and guide the design and development of tools for combating request races.}, journal={2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022)}, author={Qiu, Zhengyi and Shao, Shudi and Zhao, Qi and Khan, Hassan Ali and Hui, Xinning and Jin, Guoliang}, year={2022}, pages={744–756} }
 @article{iqbal_khan_khan_shahzad_2022, title={Left or Right: A Peek into the Political Biases in Email Spam Filtering Algorithms During US Election 2020}, DOI={10.1145/3485447.3512121}, abstractNote={Email services use spam filtering algorithms (SFAs) to filter emails that are unwanted by the user. However, at times, the emails perceived by an SFA as unwanted may be important to the user. Such incorrect decisions can have significant implications if SFAs treat emails of user interest as spam on a large scale. This is particularly important during national elections. To study whether the SFAs of popular email services have any biases in treating the campaign emails, we conducted a large-scale study of the campaign emails of the US elections 2020 by subscribing to a large number of Presidential, Senate, and House candidates using over a hundred email accounts on Gmail, Outlook, and Yahoo. We analyzed the biases in the SFAs towards the left and the right candidates and further studied the impact of the interactions (such as reading or marking emails as spam) of email recipients on these biases. We observed that the SFAs of different email services indeed exhibit biases towards different political affiliations.}, journal={PROCEEDINGS OF THE ACM WEB CONFERENCE 2022 (WWW'22)}, author={Iqbal, Hassan and Khan, Usman Mahmood and Khan, Hassan Ali and Shahzad, Muhammad}, year={2022}, pages={2491–2500} }
 @article{khan_iqbal_shahzad_jin_2022, title={RMS: Removing Barriers to Analyze the Availability and Surge Pricing of Ridesharing Services}, DOI={10.1145/3491102.3517464}, abstractNote={Ridesharing services do not make data of their availability (supply, utilization, idle time, and idle distance) and surge pricing publicly available. It limits the opportunities to study the spatiotemporal trends of the availability and surge pricing of these services. Only a few research studies conducted in North America analyzed these features for only Uber and Lyft. Despite the interesting observations, the results of prior works are not generalizable or reproducible because: i) the datasets collected in previous publications are spatiotemporally sensitive, i.e., previous works do not represent the current availability and surge pricing of ridesharing services in different parts of the world; and ii) the analyses presented in previous works are limited in scope (in terms of countries and ridesharing services they studied). Hence, prior works are not generally applicable to ridesharing services operating in different countries. This paper addresses the issue of ridesharing-data unavailability by presenting Ridesharing Measurement Suite (RMS). RMS removes the barrier of entry for analyzing the availability and surge pricing of ridesharing services for ridesharing users, researchers from various scientific domains, and regulators. RMS continuously collects the data of the availability and surge pricing of ridesharing services. It exposes real-time data of these services through i) graphical user interfaces and ii) public APIs to assist various stakeholders of these services and simplify the data collection and analysis process for future ridesharing research studies. To signify the utility of RMS, we deployed RMS to collect and analyze the availability and surge pricing data of 10 ridesharing services operating in nine countries for eight weeks in pre and during pandemic periods. Using the data collected and analyzed by RMS, we identify that previous articles miscalculated the utilization of ridesharing services as they did not count in the vehicles driving in multiple categories of the same service. We observe that during COVID-19, the supply of ridesharing services decreased by 54%, utilization of available vehicles increased by 6%, and a 5 × increase in the surge frequency of services. We also find that surge occurs in a small geographical region, and its intensity reduces by 50% in about 0.5 miles away from the location of a surge. We present several other interesting observations on ridesharing services’ availability and surge pricing.}, journal={PROCEEDINGS OF THE 2022 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI' 22)}, author={Khan, Hassan Ali and Iqbal, Hassan and Shahzad, Muhammad and Jin, Guoliang}, year={2022} }
 @article{zhao_qiu_shao_hui_khan_jin_2022, title={Understanding and Reaching the Performance Limit of Schedule Tuning on Stable Synchronization Determinism}, DOI={10.1145/3559009.3569669}, abstractNote={Deterministic MultiThreading (DMT) systems eliminate nondeterminism from the dynamic executions of multithreaded programs. They can greatly simplify multithreaded programming and ease the deployment of systems that rely on replication. We first categorize and compare existing DMT system designs along three axes, incorporating the most recent advances in DMT systems. From our study, we conclude that stable synchronization determinism is the most cost-effective design, and it is thus the focus of our work. To reduce the overhead of enforcing stable synchronization determinism, previous work has explored scheduling-based methods that tune the synchronization schedule. However, it is not clear how low the performance overhead can be through schedule tuning and how to reach the performance limit. To answer these questions, we then follow an iterative process of understanding the performance limit of schedule tuning on stable synchronization determinism and designing new scheduling policies to reach the performance limit. Through this process, we identify two types of scheduling-oblivious overheads that cannot be eliminated by schedule tuning alone. In addition, we also design a group of new policies and implement them in minSMT. Our evaluation shows that minSMT successfully reaches the performance limit of stable synchronization determinism on 107 out of 108 benchmarks after excluding the impact of scheduling-oblivious overheads, and this also results in significant performance improvements compared with state-of-the-art stable synchronization-determinism systems on 9 benchmarks. Our results also suggest that, to further improve the performance of stable synchronization determinism, future research should focus on addressing the two types of scheduling-oblivious overheads with approaches other than schedule tuning.}, journal={PROCEEDINGS OF THE 2022 31ST INTERNATIONAL CONFERENCE ON PARALLEL ARCHITECTURES AND COMPILATION TECHNIQUES, PACT 2022}, author={Zhao, Qi and Qiu, Zhengyi and Shao, Shudi and Hui, Xinning and Khan, Hassan Ali and Jin, Guoliang}, year={2022}, pages={223–238} }