Gaming environments are popular testbeds for studying human interactions and behaviors in complex artificial intelligence systems. Particularly, in multiplayer online battle arena (MOBA) games, individuals collaborate in virtual environments of high realism that involves real-time strategic decision-making and trade-offs on resource management, information collection and sharing, team synergy and collective dynamics. This paper explores whether collective intelligence, emerging from cooperative behaviours exhibited by a group of individuals, who are not necessarily skillful but effectively engage in collaborative problem-solving tasks, exceeds individual intelligence observed within skillful individuals. This is shown via a case study in League of Legends, using machine learning algorithms and statistical methods applied to large-scale data collected for the same purpose. By modeling and visualizing systematically game-specific metrics but also new game-agnostic topological and graph spectra measures of cooperative interactions, we demonstrate compelling insights about the superior performance of collective intelligence.

Contextualised word representations (e.g., ELMo and BERT) have been shown to outperform static representations (e.g., Word2vec, Fasttext, and GloVe) for many NLP tasks. In this paper, we investigate the use of contextualised embeddings for code search and classification, an area receiving less attention. We construct CodeELMo by training ELMo from scratch and fine tuning CodeBERT embeddings using masked language modeling based on natural language (NL) texts related to software development concepts and programming language (PL) texts consisting of method comment pairs from open source code bases. The dimensionality of the Finetuned Code BERT embeddings is reduced using linear transformations and augmented with a CodeELMo representation to develop CodeELBE – a lowdimensional contextualised software code representation. Results for binary classification and retrieval tasks show that CodeELBE1considerably improves retrieval performance on standard deep code search datasets compared to CodeBERT and baseline BERT models.

Abstract Approaches to evaluate comments based on whether they increase code comprehensibility for software maintenance tasks are important, but largely missing. We proposeCommentfor automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code. We conduct surveys and document developers' perceptions on the type of comments that prove useful to maintaining software in the form of comment categories. A total of 20,206 comments have been collected from open‐sourceGithubprojects and annotated with assistance from industry experts. We develop features to semantically analyze comments to locate concepts related to categories of usefulness. Additionally, features based on code and comment correlation are designed to infer whether the comment is also consistent and not superfluous. Using neural networks, comments are classified asuseful,partially useful, andnot usefulwith precision and recall scores of 86.27% and 86.42%, respectively. The proposed framework for comment quality evaluation incorporates industry practices and adds significant value to companies wanting to formulate better code commenting strategies. Furthermore, large codebases can be de‐cluttered by removing comments not helpful in maintaining code.

Regardless of attempts to extract knowledge from code bases to aid in program comprehension, there is an absence of a framework to extract and integrate knowledge to provide a near-complete multifaceted understanding of a program. To bridge this gap, we propose SMARTKT (Smart Knowledge Transfer) to extract and transfer knowledge related to software development and application-specific characteristics and their interrelationships in form of a knowledge graph. For an application, the knowledge graph provides an overall understanding of the design and implementation and can be used by an intelligent natural language query system to convert the process of knowledge transfer into a developer-friendly Google-like search. For validation, we develop an analyzer to discover concurrency-related design aspects from runtime traces in a machine learning framework and obtain a precision and recall of around 97% and 95% respectively. We extract application-specific knowledge from code comments and obtain 72% match against human-annotated ground truth.

Program comprehension is a major challenge for system maintenance. Reverse engineering has been employed for control-flow analysis of applications but not much work has been done for comprehending concurrent non-deterministic behavior of multi-threaded applications. We present D-CUBE, built using dynamic instrumentation APIs, which plugs in during execution and infers various thread models like concurrency, safety, data access, thread-pool state, exception model etc. for multi-threaded applications at runtime. We extract run-time events traced according to pre-specified logic and feed them to decision trees for inference. We use 3 benchmark suites (LOC: 50-3200) -- CDAC Pthreads benchmark [1] (18 Cases), Open POSIX Test-Suites [2] (21 Cases) and PARSEC 3.0 benchmarks [3] (3 Cases) for accuracy and volume testing and validate our approach by comparing the documented behavior of test-suites with D-CUBE's output models. We achieve over 90% accuracy. D-CUBE produces graphical event-traces with every inference for quick and effective comprehension of large code.