COBOL defects’ paper coauthored by Phase Change scientists wins IEEE distinguished paper award

October 6, 2021

tsantalis tweet congratulating paper winnersA technical paper co-authored by current and former Phase Change research scientists, and presented at the 2021 annual ICSME event, won a Distinguished Paper Award from the IEEE Computer Society Technical Council on Software Engineering (TCSE).

The paper, "Contemporary COBOL: Developers' Perspectives on Defects and Defect Location," was co-authored by current Phase Change Senior Research Scientist Rahul Pandita, former Senior Research Scientist Aleksander Chakarov, and former intern Agnieszka Ciborowska. It was presented at the 37th annual International Conference on Software Maintenance and Evolution (ICSME) 2021 in Luxembourg City, Great Duchy of Luxembourg, September 27 - October 1.

The authors presented results from surveys of COBOL and more modern programming languages regarding defects and defect-location strategies. While the software industry has made substantial advances in maintaining programs written in modern languages, mainframe programs have received limited attention. Meanwhile, mainframe systems face a critical shortage of experienced developers and replacement developers face significant difficulties even during routine maintenance tasks.

pandita tweet announcing awardPandita, who has already co-authored a number of published papers, said that this award is particularly gratifying because all of the authors were working together at Phase Change when it was written, and that he hopes it is just the first of many more like it.

This is the fourth published technical paper co-authored by Phase Change scientists, the third to be presented at scientific conferences, and the second to win a distinguished paper award.

IEEE conference accepts paper co-authored by Phase Change scientists

July 20, 2021

The International Conference on Software Maintenance and Evolution (ICSME) 2021 accepted a technical paper authored by current and former Phase Change research scientists for presentation at its 37th annual event in Luxembourg City, Great Duchy of Luxembourg, September 27 - October 1.

The paper, "Contemporary COBOL: Developers' Perspectives on Defects and Defect Location," was co-authored by current Phase Change Senior Research Scientist Rahul Pandita, former Senior Research Scientist Aleksander Chakarov, and former intern Agnieszka Ciborowska.

The authors' goal is to direct the attention of researchers and practitioners towards investigating and addressing challenges associated with mainframe software development. More specifically, they present results from surveys of COBOL and more modern programming languages regarding defects and defect-location strategies. Software development has made substantial advances in software maintenance for modern programming languages but mainframe programming languages receive limited attention.

Meanwhile, mainframe systems are facing a critical shortage of experienced developers as the current generation retires. Without extensive mainframe and application-specific experience, replacement developers face significant difficulties, even during routine maintenance tasks such as code comprehension and defect location.

ICSME is an annual event sponsored by the Institute of Electrical and Electronics Engineers (IEEE) to present, discuss, and debate the most recent ideas, experiences, and challenges in software maintenance and evolution. This year's conference will be a virtual event.

Todd Erickson is a Technology Writer with Phase Change. You can reach him at [email protected].

IEEE magazine publishes Phase Change research scientist co-authored paper

February 4, 2019

January 31, 2019

by Todd Erickson*

Phase Change research scientist Rahul Pandita’s co-written paper, “A Conceptual Framework for Engineering Chatbots,” was recently published in the November-December 2018 issue of IEEE Internet Computing^.

The industry magazine is published bi-monthly by the Institute for Electrical and Electronics Engineers (IEEE) Computer Society for evaluating and reviewing Internet-based computer applications and enabling technologies. It focuses on technologies and applications that enable practitioners to utilize Internet-based applications and tools, instead of having to build their own.

The paper

The use of chatbots as virtual assistants is becoming more widespread as companies strive to increase community engagement online and on social-media platforms.

The problem is that most commercially available bots are engineered with If-This-Then-That (IFTTT) frameworks from the 1980s. These decades-old frameworks often create inflexible chatbots that are difficult to maintain.

The bots can be monolithic and may mix dialog-managing rules with business-execution logic and response-generation rules. And when these chatbots must interact with third-party services to orchestrate workflows, the orchestration logic becomes entwined with the IFTTT rules.

Additionally, IFTTT tends to be order sensitive. As chatbots’ capabilities increase, their implementation rules grow more complex, and even simple modifications can require substantial effort.

The paper, “A Conceptual Framework for Engineering Chatbots,“ outlines a high-level conceptual framework founded upon agent-oriented abstractions – goals, plans, and commitments.

It theorizes that well-studied abstractions of goals and commitments from the area of artificial intelligence (AI) and multiagent systems allow for more flexible chatbots. Goals capture an agent’s intentions, and commitments capture meaningful business relationships between agents.

The paper describes how employing goals and commitments can enable a model chatbot that can be verified at design time or runtime, offers flexible enactments, and provides a basis for judging correctness.

Authors

In addition to Pandita, the paper is written by:

It is available free online for IEEE members, and can be purchased through the IEEE Xplore Digital Library.

*Todd Erickson is a tech writer with Phase Change Software. You can reach him at [email protected].

^The figure represented in the featured image and the IEEE Internet Computing magazine cover are copyrighted by the Institute of Electrical and Electronics Engineers Inc..

Phase Change research scientist publishes technical papers in prominent research journals

July 17, 2018

July 16, 2018

by Rahul Pandita and Todd Erickson

Phase Change research scientist Dr. Rahul Pandita recently had two co-written papers published in well-known research journals. The first paper, “Are vulnerabilities discovered and resolved like other defects?,” was published in the June 2018 volume of the Empirical Software Engineering: An International Journal and presented as a Journal First Paper at the 40th International Conference on Software Engineering (ICSE) in Gothenburg, Sweden.

The paper was co-written with Patrick Morrison, Dr. Xusheng Xiao, Dr. Ram Chillarege, and Dr. Laurie Williams. Patrick Morrison is a Ph.D. candidate in the Computer Science Department at North Carolina State University. Dr. Xusheng Xiao is an assistant professor in the Department of Electrical Engineering and Computer Science at Case Western University.

Dr. Ram Chillarege is the founder and president of Chillarege Inc. Dr. Laurie Williams is a professor, and the department head, at the North Carolina State University Department of Computer Science.

The paper

The goal of the project’s research was to determine if security defects (referred to as vulnerabilities in the paper) are discovered and resolved by different software-development practices in comparison to non-security defects. If true, technical leaders could use the distinction to drive security-specific software development process improvements.

The research consisted of extending Orthogonal Defect Classification (ODC), which is a well-established scheme for classifying software defects, to study process-related differences between vulnerabilities and non-security defects, and thereby creating ODC + Vulnerabilities (ODC+V). This new classification was applied to 583 vulnerabilities and 583 defects across 133 releases of three open-source projects – the Firefox web browser, phpMyAdmin, and Google’s Chrome web browser.

The study found that compared with non-security defects, vulnerabilities are found much later in the development cycle and are more likely to be resolved through changes to conditional logic. The results indicate opportunities may exist for more efficient vulnerability detection and resolution.

The paper was accepted by the 40th International Conference on Software Engineering (ICSE) that was held in Gothenburg Sweden, between May 27 and June 3, as part of the *ICSE 2018* Journal First Papers track. Dr. Williams presented it on May 31, 2018.

But wait, there’s more

The second paper, “Mapping the field of software life cycle security measures,” is scheduled to be published in the October 2018 issue of Information and Software Technology. It was co-written with Patrick Morrison, Dr. Laurie Williams, and David Moye, a program site lead with Aelius Exploration Technologies LLC.

The authors suspected that a catalog of software-development life cycle security metrics could assist practitioners in choosing appropriate metrics, and researchers in identifying opportunities for security measurement refinement.

They conducted a systematic mapping study, beginning with 4,818 papers and focusing on 71 papers reporting on 324 unique security metrics. For each metric, the researchers identified the subject being measured, how the metric had been validated, and how the metric was used. Then they categorized the metrics and included examples of metrics for each category.

The research found that approximately 85% of the security metrics studied were proposed and evaluated solely by their authors, leaving room for replication and confirmation through field studies. Approximately 60% of the metrics were empirically evaluated by their authors or others.

They concluded that the primary application of security metrics to the software development lifecycle is studying the relationship between properties of source code and reported vulnerabilities. This suggests that researchers need to refine vulnerability measurements and give greater attention to metrics for the requirement, design, and testing phases of development.

Rahul Pandita is a senior research scientist at Phase Change. He earned his Ph.D. in computer science from North Carolina State University. You can reach him at [email protected].

Todd Erickson is a tech writer at Phase Change. You can reach him at [email protected].

Phase Change scientists present natural language chat interface paper at AAAI Conference – blog

March 21, 2018

March 20, 2018

by Rahul Pandita and Todd Erickson

Research Scientist Aleksander Chakarov, Ph.D., presented a recently published Phase Change workshop paper at the 32nd AAAI Conference on Artificial Intelligence in February.

The AAAI conference is held each spring by the Association for the Advancement of Artificial Intelligence (AAAI) nonprofit and scientific society to promote research in artificial intelligence (AI) and scientific discussion among researchers, practitioners, scientists, and engineers in related fields.

The paper, Towards J.A.R.V.I.S. for Software Engineering: Lessons Learned in Implementing a Natural Language Chat Interface, was co-written by Chakarov and fellow research scientists Rahul Pandita and Hugolin Bergier.

"We're excited about the opportunity to share our work with researchers and get their feedback," Pandita remarked. "We consider it the first of many stepping stones to present the science behind Phase Change's technology."

Phase Change is developing a ground-breaking cognitive platform and an AI-based collaborative agent called Mia that will dramatically improve software development productivity and efficiency. Mia utilizes a natural-language chat interface so users can get up-and-running quickly.

Aleksander presented the paper on during the February 2 AAAI Workshop on NLP for Software Engineering in New Orleans, Louisiana.

The paper

Mia uses a natural language chat interface, much like the virtual assistants in other industries that have demonstrated the potential to significantly improve users' digital experiences.

The paper relates the lessons our developers learned during the first iteration of the Mia chat interface implementation, including:

  • Reusing components to quickly prototype
  • Gradually migrating from rule-based to statistical approaches
  • Adopting recommendation systems

The paper describes these lessons and others, including our experiences applying subliminal priming and the benefits of data-driven prioritization, in more detail.

The workshop

"I feel like we did a good job of setting up the context – what problems we are solving, what our approach is – and then we moved to the takeaways very quickly," Aleksander said about his experience presenting the paper. "People were engaged."

He also described two comments made during his session's brief Q&A time. The first commentator explained how current scientific research supports the paper's findings about subliminal priming and how conversations change over time.

The second commentator discussed our use of rules-based approach at first to develop an optimal work environment and then gradually moving towards a statistical approach. He suggested that there is also a third tactic that uses simulations to quickly gather data and hasten the inclusion of statistical approaches. We will investigate his suggestions for further use.

We welcome your comments and observations.

Rahul Pandita is a senior research scientist at Phase Change. He earned his Ph.D. in computer science from North Carolina State University. You can reach him at [email protected].

Todd Erickson is a tech writer with Phase Change. You can reach him at [email protected].

Phase Change scientists publish paper on lessons learned implementing a natural-language chat interface – blog

March 8, 2018

Our research scientists recently published a workshop paper on the lessons learned implementing the company's natural-language chat interface. This post summarizes the key lessons learned and identifies the open questions we faced during our initial implementation.

Phase Change is developing a ground-breaking cognitive platform and an AI-based collaborative agent, called Mia, that will dramatically improve software development productivity and efficiency. Mia utilizes natural-language processing (NLP) chatbot capabilities so new users can use the technology immediately with little or no training.

towards jarvis, lessons learned implementing NL chat interface paper

The paper, Towards J.A.R.V.I.S. for Software Engineering: Lessons Learned in Implementing a Natural Language Chat Interface, was co-written by research scientists Rahul Pandita, Aleksander Chakarov, Hugolin Bergier, and inventor and company founder Steve Bucuvalas. The full paper text is available here.

The paper

Virtual assistants have demonstrated the potential to significantly improve information technology workers' digital experiences. Mia will help software developers radically improve program comprehension. Then we will gradually expand its capabilities to include program composition and verification.

Here are a few things we learned during the first iteration of the Mia chat interface implementation.

Reuse components to quickly prototype

Instead of building everything from scratch, consider reusing existing frameworks and libraries to quickly prototype and get feedback.

Gradually migrate from rule-based to statistical approaches

With the ever-increasing popularity and efficacy of statistical approaches, teams are often tempted to implement them without enough data to design an optimal work environment.

We have noticed that recent advances in transfer learning require only a small amount of data to begin reaping the benefits of statistical approaches. However, rule-based approaches still allow prototypes to get up-and-running with only a small amount of set-up time.

A rule-based-approach also allowed us to collect more data for a better understanding of the chatbot requirements, and future positioning to effectively leverage statistical approaches.

Adopt recommendation systems

In our testing phase, we learned that although users appreciated honesty when our chatbot did not understand a request, they didn't take it well (to put it mildly) when the chatbot did not provide a way to remedy the situation.

There can be many causes for the chatbot failing to understand a request. For instance, the request might actually fall outside the chatbot's capabilities, or, in our case, one class of incomprehensible requests were due to implementation limitations.

While we can't do much about the former, building a recommendation system for the later class of requests almost always proves beneficial and vastly improves user experience.

For example, the noise in a speech-to-text (STT) component is a major cause of incomprehensible requests. In our fictional banking system, we've created software that allows pets to interact with ATMs, and a Mia user might form a query to discover all of the uses cases in which the actor "pet" participates.

If the user says: "filter by actor pet," we could expect the following transcript from the STT component, which, unfortunately, caused the subsequent pipeline components to misfire:

  • filter boy actor pet
  • filter by act or pet
  • filter by act or pad
  • filter by a store pet
  • filter by actor pass
  • filter by active pet
  • filter by actor Pat

While users will most likely be more deliberate in their subsequent interactions with the STT component, we noticed that these errors are commonplace and very negatively affected user experience.

To remedy the situation, we used a light-weight, string-similarity-based method to provide recommendations. Subsequent observations indicated that users almost always liked recommendations - except when they were too vague.

To avoid annoying users, we came up with two heuristics. First, we provided no more than three recommendations. Second, to be considered as a candidate query for recommendation, the query's similarity measure had to score higher than an empirically determined threshold with respect to incoming requests.

Over time users stop using fully formed sentences

The novelty of using a natural language interface quickly wears off. We observed that most users began sessions by forming requests with proper English sentences, but the conversation was quickly reduced to keyword utterances. Chatbot designers should plan for this eventuality. 😉

Actually, I find this quite fascinating and the natural evolution of conversation. I think of this phenomena as mirroring our natural conversations. When we first meet someone new, we are deliberate in our conversation. However, overtime, conversations are more informal. But that is a topic for future posts.
~Rahul Pandita, Phase Change research scientist
Subliminal priming

In formal conversation study, the entrainment effect is informally defined as the convergence of the vocabulary of conversation participants over a period of time to achieve effective communication. We stumbled on this effect when we observed that users employed an affected accent to get better mileage out of the STT component.

In psychology and cognitive science, subliminal priming is the phenomenon of eliciting a specific motor or cognitive response from a subject without explicitly asking for it.

We decided to see if subliminal priming would expedite entrainment. We began playing back a normalized version of a query with the query responses. That simple change led users to quickly converge to our chatbot vocabulary.

Consider the frequencies of following user request variations in our system:

Query # of users by
Test Subjects
list computations with a negative balance 30
filter for computations where output concept Balance is less than 0 17
filter by balance Less Than 0 16
filter by output concept balance is less than 0 09
show computations where output concept balance is less than 0 01
filter by output balance less than 0 224

By playing back "our system found following instances where output concept balance is less than 0," to each of these request responses, we observed that users began using the phrase "output balance less than 0," more, as shown in the frequency counts.

For the keen-eyed, notice that the repeated proper phrase, "filter by output concept balance is less than 0" is used less. However, remember that over time, users stop using fully formed sentences.

We also observed that talking with affected American or British accents works. This may be a product of an unbalanced training set used during creation of the speech-to-text models. That's why fairness testing is important. But that is yet another topic for future posts.

~Rahul Pandita
Data-driven prioritization

We also realized the benefits of leveraging data to prioritize engineering tasks as opposed to going with your gut.

A pipeline design is often a used for chatbot realization. Like most pipeline designs, the efficacy of the final product is a function of how well the individual components work in tandem within the pipeline. Thus, optimizing the design involves iteratively tuning and fixing various individual components.

So how does one decide which components to tune first? This is where data-driven prioritization can really help. For instance, in our setting, a light-weight error analysis helped on more than one occasion to identify the components we needed to focus on.

I only imagine that data-driven prioritization will become more useful in the future as we experiment with statistical approaches that often have a pipeline design.
~Rahul Pandita

We hope that our observations will be helpful for those embarking on the journey to build virtual assistants. We would love to hear your experiences.

An Analogy: Software AI and Natural Language — blog

March 6, 2017

March 6, 2017

Today's AI technology is amazing.

Only a few short years ago, only humans could interpret the meaning of text and speech. Now our cell phones understand our voices and language well enough to distinguish accents, metaphors, and sarcasm.

IBM's Watson supercomputer even understood Alex Trebek well enough to beat some of Jeopardy!'s® best players.

Computers achieve natural-language understanding through a series of logically consistent normalization steps -- starting with the processing of basic sounds to recognizing words and then understanding sentences.

If computers can understand natural language using logically consistent processes, shouldn't we be able to use similar processes to break down and normalize software?

In fact, shouldn't software be easier to normalize than the messy ambiguity of human communication?

The answer is yes.

Phase Change normalizes software source code into formal data types and organizes them into hierarchical structures that are probabilistically linked (horizontally and vertically). Our technology unlocks the vast domain and system knowledge embedded in software and makes it available to anyone involved in creating and supporting software.

To learn more about how Phase Change's revolutionary technology transforms chaotic code into coherent data and intractable software into artificially intelligent agents, read Steve Bucuvalas' paper: "An Analogy: Software AI and Natural Language."

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