Muhammad Ali Babar

Yongzheng Xie, Hongyu Zhang, Muhammad Ali Babar

Log analysis is one of the main techniques engineers use to troubleshoot faults of large-scale software systems. During the past decades, many log analysis approaches have been proposed to detect system anomalies reflected by logs. They usually take log event counts or sequential log events as inputs and utilize machine learning algorithms including deep learning models to detect system anomalies. These anomalies are often identified as violations of quantitative relational patterns or sequential patterns of log events in log sequences. However, existing methods fail to leverage the spatial structural relationships among log events, resulting in potential false alarms and unstable performance. In this study, we propose a novel graph-based log anomaly detection method, LogGD, to effectively address the issue by transforming log sequences into graphs. We exploit the powerful capability of Graph Transformer Neural Network, which combines graph structure and node semantics for log-based anomaly detection. We evaluate the proposed method on four widely-used public log datasets. Experimental results show that LogGD can outperform state-of-the-art quantitative-based and sequence-based methods and achieve stable performance under different window size settings. The results confirm that LogGD is effective in log-based anomaly detection.

Ali Kazemi Arani, Mansooreh Zahedi, Triet Huynh Minh Le et al.

Continuous Integration (CI) has become a well-established software development practice for automatically and continuously integrating code changes during software development. An increasing number of Machine Learning (ML) based approaches for automation of CI phases are being reported in the literature. It is timely and relevant to provide a Systemization of Knowledge (SoK) of ML-based approaches for CI phases. This paper reports an SoK of different aspects of the use of ML for CI. Our systematic analysis also highlights the deficiencies of the existing ML-based solutions that can be improved for advancing the state-of-the-art.

Hideaki Hata, Mingyu Guo, M. Ali Babar

Background: While bug bounty programs are not new in software development, an increasing number of companies, as well as open source projects, rely on external parties to perform the security assessment of their software for reward. However, there is relatively little empirical knowledge about the characteristics of bug bounty program contributors. Aim: This paper aims to understand those contributors by highlighting the heterogeneity among them. Method: We analyzed the histories of 82 bug bounty programs and 2,504 distinct bug bounty contributors, and conducted a quantitative and qualitative survey. Results: We found that there are project-specific and non-specific contributors who have different motivations for contributing to the products and organizations. Conclusions: Our findings provide insights to make bug bounty programs better and for further studies of new software development roles.

Zahra Mousavi, Chadni Islam, Kristen Moore et al.

The increasing trend of using Large Language Models (LLMs) for code generation raises the question of their capability to generate trustworthy code. While many researchers are exploring the utility of code generation for uncovering software vulnerabilities, one crucial but often overlooked aspect is the security Application Programming Interfaces (APIs). APIs play an integral role in upholding software security, yet effectively integrating security APIs presents substantial challenges. This leads to inadvertent misuse by developers, thereby exposing software to vulnerabilities. To overcome these challenges, developers may seek assistance from LLMs. In this paper, we systematically assess ChatGPT's trustworthiness in code generation for security API use cases in Java. To conduct a thorough evaluation, we compile an extensive collection of 48 programming tasks for 5 widely used security APIs. We employ both automated and manual approaches to effectively detect security API misuse in the code generated by ChatGPT for these tasks. Our findings are concerning: around 70% of the code instances across 30 attempts per task contain security API misuse, with 20 distinct misuse types identified. Moreover, for roughly half of the tasks, this rate reaches 100%, indicating that there is a long way to go before developers can rely on ChatGPT to securely implement security API code.

Ziyang Ye, Triet Huynh Minh Le, M. Ali Babar

Security misconfigurations in Container Orchestrators (COs) can pose serious threats to software systems. While Static Analysis Tools (SATs) can effectively detect these security vulnerabilities, the industry currently lacks automated solutions capable of fixing these misconfigurations. The emergence of Large Language Models (LLMs), with their proven capabilities in code understanding and generation, presents an opportunity to address this limitation. This study introduces LLMSecConfig, an innovative framework that bridges this gap by combining SATs with LLMs. Our approach leverages advanced prompting techniques and Retrieval-Augmented Generation (RAG) to automatically repair security misconfigurations while preserving operational functionality. Evaluation of 1,000 real-world Kubernetes configurations achieved a 94\% success rate while maintaining a low rate of introducing new misconfigurations. Our work makes a promising step towards automated container security management, reducing the manual effort required for configuration maintenance.

Jinyang Li, Sangwon Hyun, M. Ali Babar

Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation. However, the quality of the generated code is heavily dependent on the structure and composition of the prompts used. Crafting high-quality prompts is a challenging task that requires significant knowledge and skills of prompt engineering. To advance the automation support for the prompt engineering for LLM-based code generation, we propose a novel solution Diffusion-Driven Prompt Tuning (DDPT) that learns how to generate optimal prompt embedding from Gaussian Noise to automate the prompt engineering for code generation. We evaluate the feasibility of diffusion-based optimization and abstract the optimal prompt embedding as a directional vector toward the optimal embedding. We use the code generation loss given by the LLMs to help the diffusion model capture the distribution of optimal prompt embedding during training. The trained diffusion model can build a path from the noise distribution to the optimal distribution at the sampling phrase, the evaluation result demonstrates that DDPT helps improve the prompt optimization for code generation.

Triet H. M. Le, Xiaoning Du, M. Ali Babar

Collecting relevant and high-quality data is integral to the development of effective Software Vulnerability (SV) prediction models. Most of the current SV datasets rely on SV-fixing commits to extract vulnerable functions and lines. However, none of these datasets have considered latent SVs existing between the introduction and fix of the collected SVs. There is also little known about the usefulness of these latent SVs for SV prediction. To bridge these gaps, we conduct a large-scale study on the latent vulnerable functions in two commonly used SV datasets and their utilization for function-level and line-level SV predictions. Leveraging the state-of-the-art SZZ algorithm, we identify more than 100k latent vulnerable functions in the studied datasets. We find that these latent functions can increase the number of SVs by 4x on average and correct up to 5k mislabeled functions, yet they have a noise level of around 6%. Despite the noise, we show that the state-of-the-art SV prediction model can significantly benefit from such latent SVs. The improvements are up to 24.5% in the performance (F1-Score) of function-level SV predictions and up to 67% in the effectiveness of localizing vulnerable lines. Overall, our study presents the first promising step toward the use of latent SVs to improve the quality of SV datasets and enhance the performance of SV prediction tasks.

Zarrin Tasnim Sworna, Zahra Mousavi, Muhammad Ali Babar

Host based Intrusion Detection System (HIDS) is an effective last line of defense for defending against cyber security attacks after perimeter defenses (e.g., Network based Intrusion Detection System and Firewall) have failed or been bypassed. HIDS is widely adopted in the industry as HIDS is ranked among the top two most used security tools by Security Operation Centers (SOC) of organizations. Although effective and efficient HIDS is highly desirable for industrial organizations, the evolution of increasingly complex attack patterns causes several challenges resulting in performance degradation of HIDS (e.g., high false alert rate creating alert fatigue for SOC staff). Since Natural Language Processing (NLP) methods are better suited for identifying complex attack patterns, an increasing number of HIDS are leveraging the advances in NLP that have shown effective and efficient performance in precisely detecting low footprint, zero day attacks and predicting the next steps of attackers. This active research trend of using NLP in HIDS demands a synthesized and comprehensive body of knowledge of NLP based HIDS. Thus, we conducted a systematic review of the literature on the end to end pipeline of the use of NLP in HIDS development. For the end to end NLP based HIDS development pipeline, we identify, taxonomically categorize and systematically compare the state of the art of NLP methods usage in HIDS, attacks detected by these NLP methods, datasets and evaluation metrics which are used to evaluate the NLP based HIDS. We highlight the relevant prevalent practices, considerations, advantages and limitations to support the HIDS developers. We also outline the future research directions for the NLP based HIDS development.

Zarrin Tasnim Sworna, Chadni Islam, Muhammad Ali Babar

Security Orchestration, Automation, and Response (SOAR) platforms integrate and orchestrate a wide variety of security tools to accelerate the operational activities of Security Operation Center (SOC). Integration of security tools in a SOAR platform is mostly done manually using APIs, plugins, and scripts. SOC teams need to navigate through API calls of different security tools to find a suitable API to define or update an incident response action. Analyzing various types of API documentation with diverse API format and presentation structure involves significant challenges such as data availability, data heterogeneity, and semantic variation for automatic identification of security tool APIs specific to a particular task. Given these challenges can have negative impact on SOC team's ability to handle security incident effectively and efficiently, we consider it important to devise suitable automated support solutions to address these challenges. We propose a novel learning-based framework for automated security tool API Recommendation for security Orchestration, automation, and response, APIRO. To mitigate data availability constraint, APIRO enriches security tool API description by applying a wide variety of data augmentation techniques. To learn data heterogeneity of the security tools and semantic variation in API descriptions, APIRO consists of an API-specific word embedding model and a Convolutional Neural Network (CNN) model that are used for prediction of top 3 relevant APIs for a task. We experimentally demonstrate the effectiveness of APIRO in recommending APIs for different tasks using 3 security tools and 36 augmentation techniques. Our experimental results demonstrate the feasibility of APIRO for achieving 91.9% Top-1 Accuracy.

Faheem Ullah, Muhammad Ali Babar

Big Data Cyber Security Analytics (BDCA) systems use big data technologies (e.g., Apache Spark) to collect, store, and analyze a large volume of security event data for detecting cyber-attacks. The volume of digital data in general and security event data in specific is increasing exponentially. The velocity with which the security event data is generated and fed into a BDCA system is unpredictable. Therefore, a BDCA system should be highly scalable to deal with the unpredictable increase/decrease in the velocity of security event data. However, there has been little effort to investigate the scalability of BDCA systems to identify and exploit the sources of scalability improvement. In this paper, we first investigate the scalability of a Spark-based BDCA system with default Spark settings. we then identify Spark configuration parameters (e.g., execution memory) that can significantly impact the scalability of a BDCA system. Based on the identified parameters, we finally propose a parameter-driven adaptation approach, SCALER, for optimizing a system's scalability. We have conducted a set of experiments by implementing a Spark-based BDCA system on a large-scale OpenStack cluster. We ran our experiments with four security datasets. We have found that (i) a BDCA system with default Spark configuration parameters deviates from ideal scalability by 59.5% (ii) 9 out of 11 studied Spark configuration parameters significantly impact scalability (iii) SCALER improves the BDCA system's scalability by 20.8% compared to the scalability with default Spark parameter setting. The findings of our study highlight the importance of exploring the parameter space of the underlying big data framework (e.g., Apache Spark) for scalable cyber security analytics.

Yongzheng Xie, Hongyu Zhang, Bo Zhang et al.

Log analysis is an important technique that engineers use for troubleshooting faults of large-scale service-oriented systems. In this study, we propose a novel semi-supervised log-based anomaly detection approach, LogDP, which utilizes the dependency relationships among log events and proximity among log sequences to detect the anomalies in massive unlabeled log data. LogDP divides log events into dependent and independent events, then learns normal patterns of dependent events using dependency and independent events using proximity. Events violating any normal pattern are identified as anomalies. By combining dependency and proximity, LogDP is able to achieve high detection accuracy. Extensive experiments have been conducted on real-world datasets, and the results show that LogDP outperforms six state-of-the-art methods.

Mojtaba Shahin, Ali Rezaei Nasab, Muhammad Ali Babar

Software architecture is critical in succeeding with DevOps. However, designing software architectures that enable and support DevOps (DevOps-driven software architectures) is a challenge for organizations. We assert that one of the essential steps towards characterizing DevOps-driven architectures is to understand architectural design issues raised in DevOps. At the same time, some of the architectural issues that emerge in the DevOps context (and their corresponding architectural practices or tactics) may stem from the context (i.e., domain) and characteristics of software organizations. To this end, we conducted a mixed-methods study that consists of a qualitative case study of two teams in a company during their DevOps transformation and a content analysis of Stack Overflow and DevOps Stack Exchange posts to understand architectural design issues in DevOps. Our study found eight specific and contextual architectural design issues faced by the two teams and classified architectural design issues discussed in Stack Overflow and DevOps Stack Exchange into 11 groups. Our aggregated results reveal that the main characteristics of DevOps-driven architectures are: being loosely coupled and prioritizing deployability, testability, supportability, and modifiability over other quality attributes. Finally, we discuss some concrete implications for research and practice.

Fangchao Tian, Tianlu Wang, Peng Liang et al.

Software traceability plays a critical role in software maintenance and evolution. We conducted a systematic mapping study with six research questions to understand the benefits, costs, and challenges of using traceability in maintenance and evolution. We systematically selected, analyzed, and synthesized 63 studies published between January 2000 and May 2020, and the results show that: traceability supports 11 maintenance and evolution activities, among which change management is the most frequently supported activity; strong empirical evidence from industry is needed to validate the impact of traceability on maintenance and evolution; easing the process of change management is the main benefit of deploying traceability practices; establishing and maintaining traceability links is the main cost of deploying traceability practices; 13 approaches and 32 tools that support traceability in maintenance and evolution were identified; improving the quality of traceability links, the performance of using traceability approaches and tools are the main traceability challenges in maintenance and evolution. The findings of this study provide a comprehensive understanding of deploying traceability practices in software maintenance and evolution phase, and can be used by researchers for future directions and practitioners for making informed decisions while using traceability in maintenance and evolution.

Fangchao Tian, Peng Liang, Muhammad Ali Babar

Context: Software Architecture (SA) and Source Code (SC) are two intertwined artefacts that represent the interdependent design decisions made at different levels of abstractions - High-Level (HL) and Low-Level (LL). An understanding of the relationships between SA and SC is expected to bridge the gap between SA and SC for supporting maintenance and evolution of software systems. Objective: We aimed at exploring practitioners' understanding about the relationships between SA and SC. Method: We used a mixed-method that combines an online survey with 87 respondents and an interview with 8 participants to collect the views of practitioners from 37 countries about the relationships between SA and SC. Results: Our results reveal that: practitioners mainly discuss five features of relationships between SA and SC; a few practitioners have adopted dedicated approaches and tools in the literature for identifying and analyzing the relationships between SA and SC despite recognizing the importance of such information for improving a system's quality attributes, especially maintainability and reliability. It is felt that cost and effort are the major impediments that prevent practitioners from identifying, analyzing, and using the relationships between SA and SC. Conclusions: The results have empirically identified five features of relationships between SA and SC reported in the literature from the perspective of practitioners and a systematic framework to manage the five features of relationships should be developed with dedicated approaches and tools considering the cost and benefit of maintaining the relationships.

Triet H. M. Le, Bushra Sabir, M. Ali Babar

Software Engineering researchers are increasingly using Natural Language Processing (NLP) techniques to automate Software Vulnerabilities (SVs) assessment using the descriptions in public repositories. However, the existing NLP-based approaches suffer from concept drift. This problem is caused by a lack of proper treatment of new (out-of-vocabulary) terms for the evaluation of unseen SVs over time. To perform automated SVs assessment with concept drift using SVs' descriptions, we propose a systematic approach that combines both character and word features. The proposed approach is used to predict seven Vulnerability Characteristics (VCs). The optimal model of each VC is selected using our customized time-based cross-validation method from a list of eight NLP representations and six well-known Machine Learning models. We have used the proposed approach to conduct large-scale experiments on more than 100,000 SVs in the National Vulnerability Database (NVD). The results show that our approach can effectively tackle the concept drift issue of the SVs' descriptions reported from 2000 to 2018 in NVD even without retraining the model. In addition, our approach performs competitively compared to the existing word-only method. We also investigate how to build compact concept-drift-aware models with much fewer features and give some recommendations on the choice of classifiers and NLP representations for SVs assessment.

Bushra Sabir, M. Ali Babar, Raj Gaire

Adversarial Examples (AEs) generated by perturbing original training examples are useful in improving the robustness of Deep Learning (DL) based models. Most prior works, generate AEs that are either unconscionable due to lexical errors or semantically or functionally deviant from original examples. In this paper, we present ReinforceBug, a reinforcement learning framework, that learns a policy that is transferable on unseen datasets and generates utility-preserving and transferable (on other models) AEs. Our results show that our method is on average 10% more successful as compared to the state-of-the-art attack TextFooler. Moreover, the target models have on average 73.64% confidence in the wrong prediction, the generated AEs preserve the functional equivalence and semantic similarity (83.38% ) to their original counterparts, and are transferable on other models with an average success rate of 46%.

Triet H. M. Le, Hao Chen, M. Ali Babar

Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.

Nguyen Khoi Tran, Quan Z. Sheng, M. Ali Babar et al.

This article focuses on the complicated yet still relatively immature area of the Internet of Things Search Engines (IoTSE). It introduces related concepts of IoTSE and a model called meta-path to describe and classify IoTSE systems based on their functionality. Based on these concepts, we have organized the research and development efforts on IoTSE into eight groups and presented the representative works in each group. The concepts and ideas presented in this article are generated from an extensive structured study on over 200 works spanning over one decade of IoTSE research and development.

Faheem Ullah, M. Ali Babar

Context: Big Data Cybersecurity Analytics is aimed at protecting networks, computers, and data from unauthorized access by analysing security event data using big data tools and technologies. Whilst a plethora of Big Data Cybersecurity Analytic Systems have been reported in the literature, there is a lack of a systematic and comprehensive review of the literature from an architectural perspective. Objective: This paper reports a systematic review aimed at identifying the most frequently reported quality attributes and architectural tactics for Big Data Cybersecurity Analytic Systems. Method: We used Systematic Literature Review (SLR) method for reviewing 74 primary studies selected using well-defined criteria. Results: Our findings are twofold: (i) identification of 12 most frequently reported quality attributes and the justification for their significance for Big Data Cybersecurity Analytic Systems; and (ii) identification and codification of 17 architectural tactics for addressing the quality attributes that are commonly associated with Big Data Cybersecurity Analytic systems. The identified tactics include six performance tactics, four accuracy tactics, two scalability tactics, three reliability tactics, and one security and usability tactic each. Conclusion: Our findings have revealed that (a) despite the significance of interoperability, modifiability, adaptability, generality, stealthiness, and privacy assurance, these quality attributes lack explicit architectural support in the literature (b) empirical investigation is required to evaluate the impact of codified architectural tactics (c) a good deal of research effort should be invested to explore the trade-offs and dependencies among the identified tactics and (d) there is a general lack of effective collaboration between academia and industry for supporting the field of Big Data Cybersecurity Analytic Systems.

Aakash Ahmad, Muhammad Ali Babar

Software architecture related issues are important for robotic systems. Architecture centric development and evolution of software for robotic systems has been attracting researchers attention for more than two decades. The objective of this work is to systematically identify, taxonomically classify and holistically map existing solutions, research progress and trends that influence architecture-driven modeling, development and evolution of robotic software. We carried out a Systematic Mapping Study to identify and analyze the relevant literature based on 56 peer-reviewed papers. We extract and synthesize the data from selected papers to taxonomically classify the existing research and systematically map the solutions, frameworks, notations and evaluation methods to highlight the role of software architecture in robotic systems. We have identified eight distinct research themes that support architectural solutions to enable operations, evolution and development specific activities of robotic software. The research in this area has progressed from object oriented to component based and now to service driven robotics representing different architectural generations. The reported solutions have exploited model-driven, service oriented and reverse engineering techniques since 2005. An emerging trend is cloud robotics that exploits the foundations of service driven architectures to support an interconnected web of robots. The results of this SMS facilitate knowledge transfer, benefiting researchers and practitioners, focused on exploiting software architecture to model, develop and evolve robotic systems.