The Java platform and its third-party libraries provide useful features to facilitate secure coding. However, misusing them can cost developers time and effort, as well as introduce security vulnerabilities in software. We conducted an empirical study on StackOverflow posts, aiming to understand developers' concerns on Java secure coding, their programming obstacles, and insecure coding practices.

Programmers produce code clones when developing software. By copying and pasting code with or without modification, developers reuse existing code to improve programming productivity. However, code clones present challenges to software maintenance: they may require consistent application of the same or similar bug fixes or program changes to multiple code locations. To simplify the maintenance process, various tools have been proposed to automatically detect clones [1], [2], [3], [4], [5], [6]. Some tools tokenize source code, and then compare the sequence or frequency of tokens to reveal clones [1], [3], [4], [5]. Some other tools detect clones using tree-matching algorithms to compare the Abstract Syntax Trees (ASTs) of source code [2], [6]. In this paper, we present CCLEARNER, the first solely token-based clone detection approach leveraging deep learning. CCLEARNER extracts tokens from known method-level code clones and nonclones to train a classifier, and then uses the classifier to detect clones in a given codebase. To evaluate CCLEARNER, we reused BigCloneBench [7], an existing large benchmark of real clones. We used part of the benchmark for training and the other part for testing, and observed that CCLEARNER effectively detected clones. With the same data set, we conducted the first systematic comparison experiment between CCLEARNER and three popular clone detection tools. Compared with the approaches not using deep learning, CCLEARNER achieved competitive clone detection effectiveness with low time cost.

The performance of in-memory based data analytic frameworks such as Spark is significantly affected by how data is partitioned. This is because the partitioning effectively determines task granularity and parallelism. Moreover, different phases of a workload execution can have different optimal partitions. However, in the current implementations, the tuning knobs controlling the partitioning are either configured statically or involve a cumbersome programmatic process for affecting changes at runtime. In this paper, we propose CHOPPER, a system for automatically determining the optimal number of partitions for each phase of a workload and dynamically changing the partition scheme during workload execution. CHOPPER monitors the task execution and DAG scheduling information to determine the optimal level of parallelism. CHOPPER repartitions data as needed to ensure efficient task granularity, avoids data skew, and reduces shuffle traffic. Thus, CHOPPER allows users to write applications without having to hand-tune for optimal parallelism. Experimental results show that CHOPPER effectively improves workload performance by up to 35.2% compared to standard Spark setup.